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      Current challenges and implications for dengue, chikungunya and Zika seroprevalence studies worldwide: A scoping review

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          Abstract

          Background

          Arboviral infections are a public health concern and an escalating problem worldwide. Estimating the burden of these diseases represents a major challenge that is complicated by the large number of unapparent infections, especially those of dengue fever. Serological surveys are thus required to identify the distribution of these diseases and measure their impact. Therefore, we undertook a scoping review of the literature to describe and summarize epidemiological practices, findings and insights related to seroprevalence studies of dengue, chikungunya and Zika virus, which have rapidly expanded across the globe in recent years.

          Methodology/Principal findings

          Relevant studies were retrieved through a literature search of MEDLINE, WHOLIS, Lilacs, SciELO and Scopus (2000 to 2018). In total, 1389 publications were identified. Studies addressing the seroprevalence of dengue, chikungunya and/or Zika written in English or French and meeting the inclusion and exclusion criteria were included. In total, 147 studies were included, from which 185 data points were retrieved, as some studies used several different samples. Most of the studies were exclusively conducted on dengue (66.5%), but 16% were exclusively conducted on chikungunya, and 7 were exclusively conducted on Zika; the remainder were conducted on multiple arboviruses. A wide range of designs were applied, but most studies were conducted in the general population (39%) and in households (41%). Although several assays were used, enzyme-linked immunosorbent assays (ELISAs) were the predominant test used (77%). The temporal distribution of chikungunya studies followed the virus during its rapid expansion since 2004. The results revealed heterogeneity of arboviruses seroprevalence between continents and within a given country for dengue, chikungunya and Zika viruses, ranging from 0 to 100%, 76% and 73% respectively.

          Conclusions/Significance

          Serological surveys provide the most direct measurement for defining the immunity landscape for infectious diseases, but the methodology remains difficult to implement. Overall, dengue, chikungunya and Zika serosurveys followed the expansion of these arboviruses, but there remain gaps in their geographic distribution. This review addresses the challenges for researchers regarding study design biases. Moreover, the development of reliable, rapid and affordable diagnosis tools represents a significant issue concerning the ability of seroprevalence surveys to differentiate infections when multiple viruses co-circulate.

          Author summary

          Arthropod-borne viruses (arboviruses) are among the most important of the emerging infectious disease public health problems facing the world. The actual impact of arboviruses worldwide remains unknown, and estimating the true burden of these diseases represents a current challenge. Serological surveys are the most reliable tool for estimating the impact of arboviruses outbreaks in a given territory, and the results of such surveys have implications for potential mitigation measures such as vaccination. We undertook a thorough review of the literature produced from 2000 to March 15, 2018, addressing the seroprevalence of dengue, chikungunya and/or Zika to describe and summarize methodological approaches and map the geographical distribution of seroprevalence studies for these three viruses worldwide. A total of 185 studies addressing the seroprevalence of dengue, chikungunya and/or Zika were included in the review. Most of the studies were exclusively conducted on dengue (66.5%), but 16% were exclusively conducted on chikungunya, and 7 studies were exclusively conducted on Zika; the remainder were conducted on multiple arboviruses. Our study reveals that a wide range of methodological designs were applied regarding population, recruitment and/or laboratory testing. This study also highlights the high seroprevalence heterogeneity between continents and within a given country for dengue, chikungunya and Zika viruses. The results underscore existing gaps in seroprevalence studies distribution worldwide and the need to develop the most sensitive and specific diagnosis tool to provide recommendations for future serological studies.

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          Most cited references 161

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          The global emergence/resurgence of arboviral diseases as public health problems.

          During the past 20 years there has been a dramatic resurgence or emergence of epidemic arboviral diseases affecting both humans and domestic animals. These epidemics have been caused primarily by viruses thought to be under control such as dengue, Japanese encephalitis, yellow fever, and Venezuelan equine encephalitis, or viruses that have expanded their geographic distribution such as West Nile and Rift Valley fever. Several of these viruses are presented as case studies to illustrate the changing epidemiology. The factors responsible for the dramatic resurgence of arboviral diseases in the waning years of the 20th century are discussed, as is the need for rebuilding the public health infrastructure to deal with epidemic vector-borne diseases in the 21st century.
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            Gender and health: an update on hypotheses and evidence.

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              Economic and Disease Burden of Dengue in Southeast Asia

              Introduction Dengue fever is among the most important infectious diseases in tropical and subtropical regions of the world, and represents a significant economic and disease burden in endemic countries [1]–[4]. There are about 100–200 million infections per year in more than 100 countries [5]. Estimating the economic and disease burden of dengue is critical to inform policy makers, set health policy priorities, and implement disease-control technologies. Here we estimate the economic and disease burden of dengue in 12 countries of Southeast Asia (SEA). We included all countries in the Association of Southeast Asian Nations [6], plus Bhutan and East-Timor due to their geographic proximity, to be consistent with our study on the incidence of dengue in the region [7]. Our study area comprises the following 12 countries: Bhutan, Brunei, Cambodia, East-Timor, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Viet Nam. Studying dengue burden in SEA is important for several reasons. Dengue is among the greatest disease burdens in SEA, and has been hyperendemic for decades [8]–[11]. SEA is the region with the highest dengue incidence, with cycles of epidemics occurring every three to five years [1], [8]. The WHO regions of SEA and the Western Pacific represent about 75% of the current global burden of dengue [12], [13]. Recent studies have estimated economic burden of dengue in specific countries of SEA (costs in 2010 US dollars [14]). For example, using the average reported cases between 2001–2005, Suaya et al. [2] estimated that the annual costs for dengue illness (standard errors in parenthesis) in Cambodia, Malaysia, and Thailand were at least US$3.1 (±0.2), US$42.4 (±4.3), and US$53.1 (±11.4) million (m), respectively. Beaute and Vong estimated an annual cost (2006–2008) of US$8.0m for Cambodia [15]. Adjusting the officially reported cases in 2009 with expansion factors (EFs) derived from a Delphi process, Shepard et al. [16] estimated that the annual cost of dengue in Malaysia, as updated [17], was about US$103.4m per year (range: US$78.8m–US$314.2m). Lim et al. [18] estimated a yearly cost of dengue–including dengue illness, vector control, and research and development activities–of US$133m (range: US$88m–US$215m) in Malaysia (2002–2007) and US$135m (range: US$56m–US$264m) in Thailand (2000–2005), respectively, in which dengue illness represented about 41.3% of the total costs (US$54.9m) in Malaysia and 49% (US$66.2m) in Thailand. Based on data from a provincial hospital, Kongsin et al. [19] estimated that the total economic burden of dengue in Thailand was US$175.4m (standard deviation: US$36.6m), of which US$126.3m corresponded to dengue illness and US$49.1m to dengue control. In Singapore, Carrasco et al. [20] estimated that yearly dengue illness costs US$41.5m and vector control costs US$50.0m. Last, Luong et al. [21] obtained an average annual cost (2004–2007) of US$30.3m for Viet Nam. The dengue burden of disease (number of disability adjusted life years or DALYs, based on the original 1994 definition [22] and extrapolated to 2010 based on population) has also been estimated for Cambodia (8,200 [15]), Myanmar (3,900 [23]), Singapore (700 [20]), and Thailand (28,900 [24]; 32,500 [25]). The few published estimates of economic and disease burden of dengue in SEA are based on a single or a small number of countries, and the comparison of estimates is limited by methodological differences between studies. Previous multi-country studies of dengue burden include the economic impact of dengue in the Americas [3], and an eight-country study including five countries in the Americas and three in SEA [2]. This paper aims to reduce this gap by estimating the economic and disease burden of dengue illness in SEA using a consistent methodology. Methods The economic burden of dengue is calculated as the total number of dengue cases times the total costs per dengue episode. To calculate the disease burden, an estimate of the total DALY burden per cases is also required. Total number of dengue cases Because dengue is an infectious disease, there is considerable annual variability in the number of dengue cases. We used the average officially reported cases in 2001–2010 to obtain a more stable estimate for each country. We obtained the number of reported dengue cases from various sources, including data from the country's Ministry of Health or statistics agency, WHO, or published studies [12], [16], [26]–[35]. Dengue is a reportable illness in SEA and thus the number of cases reported is correlated to the total cases. However, there is substantial underreporting of symptomatic dengue fever in SEA, and official statistics commonly underestimate case rates [7], [36]. Estimating the total number of dengue cases is challenging due to the limits of passive surveillance systems, which are useful to detect dengue outbreaks and to understand long-term trends of symptomatic infection, but underestimate the true incidence. The rate of reporting of surveillance systems depends on several variables, including the severity of dengue, identification method (e.g., clinical diagnosis, laboratory test), treatment facilities, year of data collection, the area where dengue is measured, among others [16], [27]. Recent studies have improved the estimate of the total number of cases by using EFs [3], [7], [16], [20], the ratio of the best estimate of the total number of symptomatic dengue, divided by the number of reported cases. We adjusted the officially reported cases using Undurraga et al.'s estimates of EFs for ambulatory, hospitalized, and total dengue episodes to estimate the incidence of dengue by country [7]. Undurraga et al. estimated the annual average of dengue episodes based on the officially reported cases from 2001 through 2010, and derived country-specific EFs through a systematic analysis of published studies that reported original, empirically derived EFs or the necessary data to obtain them. Costs per dengue episode To estimate the economic burden of symptomatic dengue infection one requires information on the unit costs of providing inpatient and outpatient medical care, in both private and public facilities. We conducted a systematic literature review for articles on the economic costs of dengue in Southeast Asia published between 1995 and 2012 using Web of Science and MEDLINE (72 articles), and PubMed (97 articles) using the keywords dengue, health, and economics. We reviewed the abstracts of these articles and identified 11 articles that explicitly reported data on the economic costs per dengue fever episode, or included the necessary information to estimate them [2], [15], [23], [24], [37]–[43]. To these articles, we added nine recently published articles [16], [19], [20], [44], or found in previous searches [21], [25], [45]–[47]. Although this study is an original research study and not a systematic review, we adapted relevant parts of the PRISMA check list and flowchart to our literature review (Figure S1, Table S1) [48]. We then filtered these 20 articles based on the following criteria: (1) use of original, empirical data; (2) use of a scientifically consistent approach; (3) use of externally valid and representative data; and (4) use of recent data in order to reflect current medical practice and technology. We selected studies that scored well, albeit not perfectly, on these criteria, providing what we think are the best data available. For each of these countries we derived the best cost estimate for direct medical and non-medical costs and indirect costs, for both inpatient and outpatient treatment. For countries in which no cost data were available, we relied instead on expert opinion (Malaysia) or in the extrapolation of data based on regression analysis (Bhutan, Brunei, East Timor, Indonesia, Laos, Myanmar, and Philippines), using unit costs as the dependent variable and gross domestic product (GDP) per capita as the independent variable. We found six studies that included dengue costs for Cambodia [2], [15], [37], [39], [40], [44]. Our best estimates for direct costs are based on the average between the costs estimates of two studies by Suaya et al. [39], [44]; to estimate indirect costs we used an average between these two studies plus the estimates by Huy et al. [37]. In the first study, Suaya et al. estimated costs based on patient interviews and record reviews of hospitalized patients from Daun Keo Referral Hospital [44]. In the second study considered, the authors' estimates were based on expert opinion and interviews with families, and contrasted with survey data from hospitalized patients and financial data from the National Pediatric Hospital [39]. Two additional studies estimated out-of-pocket expenditures, which may not necessarily reflect the real costs of a dengue episode [37], [40]. We used Huy et al.'s estimates to obtain indirect costs per dengue episode [37]. As Beaute and Vong's estimates were based on secondary analysis of data, they were excluded [15]. For Viet Nam, our best cost estimates were based on the results from an unpublished multicenter cost study in southern Viet Nam by Luong et al. [21], which included data on medical expenditures from four hospitals, transportation costs, and household impact. Patients were recruited based on severity, age, and type of setting, and adjusted the costs accordingly. Another study based on Viet Nam also provided detailed data on dengue; however, it was restricted only to dengue hemorrhagic fever (DHF) cases in children 15 yrs) based on data by the National Surveillance System (2004–2010). f The data by Kongsin et al. [19] are the same as the data used by Suaya et al. [2]. The costs per ambulatory case were estimated as 25% of those per hospitalized case based on Shepard et al. [49]. g Estimate for patients aged 18–64 years based on transport costs, average productivity loss per day, and household services lost per day. For hospitalized patients, the estimate considers the average number of days a person is hospitalized per dengue episode, and for ambulatory patients, the total number of visits per episode. Results The average annual number of reported cases in SEA was 386,000 patients (2001–2010), and 2,126 deaths. Using corresponding EFs, we obtained a yearly average of about 2.9 m cases of dengue illness in SEA (0.8 m hospitalized and 2.1 m ambulatory patients), 5,906 deaths, and a weighted overall EF of 7.6. Table 1 shows the annual average number of reported dengue cases in SEA (2001–2010), the estimated hospitalized, ambulatory, and total number of dengue cases, and the total number of deaths, using country-specific EFs. The lower and upper ranges for each of our estimates are shown in parentheses. Our literature review yielded 20 studies on unit costs per dengue episode [2], [15], [16], [19], [21], [23]–[25], [37]–[47]. We extracted data from the articles using a template similar to Table 2, with additional columns (e.g., date the article was reviewed, limitations). After applying our filtering criteria, we had sound data for five countries-Cambodia, Viet Nam, Malaysia, Thailand, and Singapore-one for each category of income-level defined by the World Bank (e.g., low-income country) [68], which makes our extrapolated estimates more consistent. Table 2 shows a summary of our best estimates for the unit costs per dengue episode for each country (2010 US dollars). While the summary data may not necessarily be representative of each country, to our knowledge they are the best cost data available. Table 3 shows the predicted values of direct and indirect unit costs per dengue case based on the linear regression estimates (R2 = 0.94 and 0.87, respectively), for those countries for which we did not have empirical data. Figure 1 and Figure 2 show the relation between GDP per capita and unit direct and indirect costs per episode respectively, and the 95% CI for each set of estimates. 10.1371/journal.pntd.0002055.g001 Figure 1 Direct costs per non-fatal dengue episode for hospitalized and ambulatory cases by per capita GDP (2010 US$). Source: Authors' calculations from [2], [16], [17], [19]–[21], [37], [39], [42]–[44], [47]. 10.1371/journal.pntd.0002055.g002 Figure 2 Indirect costs per non-fatal dengue episode for hospitalized and ambulatory cases by per capita GDP (2010 US$). Source: Authors' calculations from [2], [16], [17], [19]–[21], [37], [39], [42]–[44], [47]. 10.1371/journal.pntd.0002055.t003 Table 3 Predicted values of direct and indirect unit costs per dengue case, based on linear regression estimates (2010 US dollars). Country GDP per capita World Bank classification Direct Costs Indirect Costs Hosp. Amb. Hosp. Amb. Bhutan 2,010 Lower-middle 172.8 46.1 34.5 16.2 Brunei 28,832 High 1,747.4 465.8 733.6 343.9 Cambodiaa 791b Low 84.1 18.8 31.9 4.6 East Timor 571b Lower-middle 57.9 15.4 8.1 3.8 Indonesia 2,890 Lower-middle 236.8 63.1 52.3 24.5 Laos 976b Lower-middle 92.2 24.6 15.0 7.0 Malaysiaa 8,184 Upper-middle 659.9 244.2 203.3 178.0 Myanmar 721b Low 70.9 18.9 10.6 5.0 Philippines 2,063 Lower-middle 176.7 47.1 35.5 16.6 Singaporea 41,893b High 2,060.5 394.9 948.0 873.4 Thailanda 4,850 Upper-middle 584.9 146.2 50.0 12.5 Viet Nama 1,141b Lower-middle 63.7 21.6 12.7 9.9 a Unit costs were obtained from empirical data and not from extrapolation. b International Monetary Fund (IMF) estimate for 2010. Notation: GDP denotes gross domestic product; Hosp. denotes Hospitalized; Amb. denotes Ambulatory. Source: IMF [14]; World Bank [68]; and cost data sources shown in Table 2 [2], [16], [17], [19]–[21], [37], [39], [42]–[44], [47]. Economic and disease burden of dengue in SEA Table 4 shows the average total annual economic and disease burden of dengue by country. The table includes the 95% certainty level bounds obtained using 1,000 Monte Carlo simulations in parenthesis under each estimate. Using our best estimates for the total number of cases and the unit cost per dengue episode, we obtained an overall annual economic burden of dengue of US$950 million (m) (US$610m–US$1,384m). The average annual direct costs amounted to US$451m (US$289m–US$716m) and the indirect costs were US$499m (US$290m–US$688m). Indonesia was the country with the highest economic burden of dengue in the region, followed by Thailand, representing about 34% and 31% of the total economic burden of dengue, respectively. The average population for SEA in the years considered was about 574 m people [70]–[72]; hence the cost of dengue illness was about US$1.65 per capita (US$1.06–US$2.41). The costs per capita by country ranged from US$0.28 (US$0.19–US$0.39) in Viet Nam to US$14.99 (US$9.37–US$21.10) in Singapore. 10.1371/journal.pntd.0002055.t004 Table 4 Annual dengue economic and disease burden in DALYs, by country (average, 2001–2010). Country Population (1,000 s) Aggregate costs (2010 US$, 1,000 s) Cost per capita (2010 US$) DALYS Direct Indirect Total Bhutan 726 59 238 295 0.41 148 (39–84) (135–319) (183–389) (0.25–0.54) (86–198) Brunei 378 223 412 636 1.69 14 (154–296) (268–520) (441–802) (1.17–2.12) (9–19) Cambodia 13,670 6,264 10,317 16,540 1.21 15,452 (2,899–10,663) (3,890–19,558) (7,763–29,598) (0.57–2.17) (5,910–29,202) East Timor 1,061 163 199 363 0.34 417 (90–284) (119–257) (231–529) (0.22–0.50) (249–563) Indonesia 232,462 93,470 229,199 323,163 1.39 95,168 (64,017–130,726) (127,273–281,114) (205,440–407,748) (0.88–1.75) (52,759–117,836) Laos 5,931 3,427 1,654 5,093 0.86 2,369 (2,273–4,643) (1,154–2,125) (3,592–6,717) (0.61–1.13) (1,457–3,162) Malaysia 27,051 64,426 63,431 127,973 4.73 8,324 (47,195–98,585) (48,377–89,790) (90,478–181,432) (3.34–6.71) (5,517–12,393) Myanmar 46,916 6,917 7,607 14,476 0.31 13,620 (4,094–10,841) (4,675–10,083) (9,393–20,006) (0.20–0.43) (8,006–18,205) Philippines 88,653 20,656 60,740 80,829 0.91 37,685 (14,685–27,365) (35,148–79,301) (52,126–103,948) (0.59–1.17) (22,089–49,617) Singapore 4,476 25,156 42,076 67,090 14.99 1,089 (14,363–38,944) (26,751–56,578) (41,946–94,430) (9.37–21.10) (660–1,509) Thailand 67,796 215,722 74,303 290,028 4.28 28,475 (134,028–375,270) (39,335–139,060) (181,559–505,186) (2.68–7.45) (16,505–49,552) Viet Nam 85,007 14,814 8,659 23,453 0.28 11,079 (10,103–21,468) (6,269–11,890) (16,463–33,099) (0.19–0.39) (7,226–16,452) Total 574,236 451,297 498,836 949,940 1.65 213,839 (289,492–715,924) (290,043–688,415) (609,614–1,383,882) (1.06–2.41) (120,472–298,709) Note: Cost estimates and their corresponding 95% certainty levels (in parentheses), were obtained using 1,000 Monte Carlo simulations with the simultaneous variation of expansion factors (EFs), the share of hospitalized cases, unit costs for ambulatory and hospitalized cases, and disability-adjusted life years (DALYs). We obtained an annual average of 214,000 DALYs (range: 120,000–299,000 DALYs) for SEA (Table 4), which is equivalent to 372 DALYs per million inhabitants (range: 210–520). About 45% of the total disease burden in the region is incurred by Indonesia, followed by the Philippines with about 18% of the total. Using the original 1994 definition [22], the rate of DALYs per million population for dengue in SEA ranks higher than that of 17 of the 39 health conditions in SEA and the Western Pacific combined, including poliomyelitis (1 per m), Japanese encephalitis (199 per m), otitis media (219 per m), upper respiratory infections (222 per m), hepatitis B (349 per m). Compared to other neglected tropical diseases in this combined region, dengue ranks higher than schistosomiasis (4 per m), leprosy (38 per m), trachoma (149 per m), trichuriasis (188 per m), hookworm (191 per m), and ascariasis (209 per m). Dengue ranks just under leishmaniasis (386 per m) and malaria (443 per m) [57]. Discussion Our results show that dengue represents a substantial economic and disease burden in SEA. We combined multiple sources of data to quantify this burden. On average, about 52% of the total economic costs of dengue resulted from productivity lost (indirect costs), including non-fatal and fatal cases. The average per capita economic cost of dengue illness represents about 0.03% of the average per capita GDP in the region (in 2010), and total disease burden is 214,000 DALYs per year. Indonesia has a higher share of disease burden than economic burden, which is partly explained by the relatively lower costs per dengue episode. We used the average number of cases of dengue between 2001 and 2010 to obtain a stable estimate of the burden of dengue, which we consider more useful for policy purposes than an estimate for a specific year. Figure 3 shows the annual variation of total estimated dengue cases and economic burden of dengue in SEA. We are assuming that the EFs and unit costs are constant for all years. As expected, total costs are highly correlated with total number of cases (R2 = 0.94, p<0.001); however, the relation depends on which countries are facing an epidemic. While dengue epidemics in the region follow a similar pattern, total costs increase more sharply when the epidemic affects higher-income countries. For example, we estimated fewer dengue episodes in year 2005 (2.37 m) than in 2006 (2.46 m), but because the epidemic affected richer countries in 2005 (e.g., Singapore and Thailand) than in 2006 (e.g., Viet Nam, Indonesia, Cambodia, Philippines), the aggregate costs were higher in 2005 (US$1.02billion) than in 2006 (US$0.84billion). The costs for year 2005 were similar to those in 2008 (US$1.01billion) and 2009 (US$1.02), but the number of cases was much lower in 2005 (2.37 m) than in 2008 (3.37 m) and 2009 (3.42 m), when the dengue epidemic peaked in the poorer countries (e.g., Indonesia, Myanmar). 10.1371/journal.pntd.0002055.g003 Figure 3 Aggregate values of dengue episodes and economic burden by year for 12 countries in SEA (2001–2010). Source: Authors' calculations. We found substantial variability in the costs per dengue episode. There was also considerable variability in the country-specific EFs, as has been discussed elsewhere [7]. These variations were addressed using probabilistic analysis; however, costs per episode and EFs remain an area of uncertainty for most of the countries we considered. Our estimates of economic and disease burden of dengue are consistent with previous estimates from published studies (Table 5). Our estimates of economic burden, without considering costs such as prevention or vector control, for Cambodia, Malaysia, Singapore, and Thailand are higher than in previous studies [2], [16]–[20], and lower than a previous estimate in Viet Nam [21]. Compared to these studies, our higher estimates of economic burden arise mainly because previous studies did not adjust for underreporting of dengue episodes [2], [23], used smaller EFs [16]–[19], considered year intervals with lower reported dengue [18], estimated lower indirect costs [15], estimated productivity loss based on the minimum wage [16], [17], did not consider fatal cases [18], or adjusted for underreporting only of non-fatal cases [20]. Compared to previous estimates of disease burden, our estimates were higher for Myanmar [23], Singapore [20], and Cambodia [15], and lower for Thailand [24], [25]. Our higher estimate for DALYs were partly explained because the previous study for Myanmar only included DHF, did not correct for underreporting, and considered almost 30 years of reporting, which lowered the average reported cases [23], and the estimate for Singapore [20] did not consider an EF for fatal cases of dengue. 10.1371/journal.pntd.0002055.t005 Table 5 Comparison of estimates of annual economic and disease burden of dengue with previous studies, by country. Economic burden (US$, million) Disease burden (DALYsa) Years considered Source Cambodia 16.5 15,425 2001–2010 Present study 3.1 2001–2005 Suaya et al., 2009 [2] 8.0 8,243 2006–2008 Beaute and Vong, 2010 [15] Malaysia 128.0 8,324 2001–2010 Present study 42.4 2001–2005 Suaya et al., 2009 [2] 54.9 2002–2007 Lim et al., 2010 [18] 103.4 2009 Shepard et al. [16], updated 2013 [17] Myanmar 14.5 13,620 2001–2010 Present study 3,933b 1970–1997 Cho Min Naing, 2000 [23] Singapore 67.1 1,089 2001–2010 Present study 41.5c 734c 2000–2009 Carrasco et al.,2011 [20] Thailand 290.0 28,475 2001–2010 Present study 66.2 2000–2005 Lim et al., 2010 [18] 53.1 2001–2005 Suaya et al., 2009 [2] 126.3 2001–2005 Kongsin et al., 2010 [19] 31,546 1998–2002 Anderson et al., 2007 [25] 28,949 2001 Clark et al., 2005 [24] Viet Nam 23.5 11,079 2001–2010 Present study 30.3 2004–2007 Luong et al., 2012 [21] a Estimates of the number of disability-adjusted life years (DALYs) were extrapolated to 2010 based on population. b DALY estimates only include dengue hemorrhagic fever (DHF) episodes. c The economic and disease burden estimates correspond to Carrasco et al.'s estimates [20], based on the same methods and assumptions than those we used. Economic burden was based on the human capital approach, but Carrasco et al. also estimated annual economic burden of dengue using the friction cost method (US$35.1 million). Similarly, disease burden was estimated using disability weights from previous literature (with an age-weighting constant C = 1), but Carrasco et al. also estimated DALYs using disability weights from WHO and quality of life-based disability weights, and estimated DALYs with C = 1 and C≠1). The cost per capita associated to dengue in SEA was 68% of that found for the Americas as a whole (US$2.42; range: 1.01–4.47), but DALYs per m were 4.6 times higher than in the Americas (81 DALYs per m; range: 50–131 [3]; WHO's estimate was 73 DALYs per m [57]). This is partly explained by the higher incidence rates of DHF and dengue shock syndrome (DSS) in SEA, which together are approximately 18 times higher than that in the Americas [9], and the case fatality rate is 29 times higher (the estimated case fatality rate was 8/100,000). Also, the main drivers of cost in SEA and the Americas are Indonesia (27% of the total cases of dengue) and Brazil (39% of total cases), respectively. Brazil's GDP per capita is about 3.6 times that of Indonesia's [14] so the average cost per dengue case in the former is substantially higher. Our estimate of the absolute dengue disease burden of 214,000 DALYs in SEA alone is higher than that of the worldwide disease burden (DALYs) of poliomyelitis (34,000), diphtheria (174,000), or leprosy (194,000) [57]. The DALY rate per population of dengue (372 per million) exceeds that of other diseases of public health importance including Japanese encephalitis, upper respiratory infections, and hepatitis B, and other neglected tropical diseases such as ascariasis, trichuriasis, or hookworm for the combined WHO regions containing SEA. These results have some limitations and areas of uncertainty. First, the EFs we used to adjust for underreporting were derived from several empirical studies in countries of SEA that used different methodologies (e.g., cohort studies, capture-recapture, hospital records), and some differ in the age groups, or severity of dengue reported [7]. The rate of underreporting also depends on several factors including year of data collection, sample demographics, specific region, vector control activities, disease awareness, quality of the surveillance system. Due to paucity of data, we assumed that the rate of underreporting was constant for each country in SEA during the years considered in this study. Second, we assumed that the average unit costs of inpatient and outpatient treatments of dengue illness were constant across years. Our cost estimates were obtained from empirical studies that in some cases were limited to specific regions or facility types. We could further refine these cost estimates by adjusting other variables such as region, number of specialist physicians, healthcare system, and treatment and technology changes that might have developed since the reference study took place. These levels of detail were not available, but we obtained our estimates from the best accessible data. Third, because there were no studies for all countries in SEA, we had to extrapolate data based on similarities between countries, such as GDP per capita in the case of cost, and an index of healthcare quality for EFs [7]. Fourth, because we lacked more detailed data, we assumed that the age distribution of fatal cases was the same as the age distribution of dengue incidence. This is a conservative assumption, as existing literature suggests that severe episodes of dengue illness in SEA affect mostly infants and children [9], [13], [73], [74], and that children are more vulnerable than adults to shock syndrome [75]. Hence, we would expect the very young to have higher death rates than the rest of the population and therefore, the economic and disease burden might be even higher. Fifth, because the incidence of dengue varies considerably from year to year, we used the average cases of dengue between 2001 and 2010 to obtain more stable estimates. This averaging probably makes our estimates of dengue burden conservative, since several studies indicate that the total number of episodes of symptomatic dengue is increasing [5], [13], [74], [76]. Last, our estimates of the economic and disease burden of dengue illness were based on previous studies that considered the acute symptoms of dengue [2], [77]–[79]. A few recent studies suggest that dengue patients may present long-term symptoms [80]–[84], but there is yet no agreement on the frequency, intensity, or duration of these long-term consequences of dengue infection, sometimes referred to as Dengue Chronic Fatigue Syndrome [83]. If long-term sequelae of dengue are common and affect people's ability to work, then existing studies would be systematically underestimating the economic and disease burden. There was still too much uncertainty over the long-term sequelae of dengue to consider it in our calculations while being conservative. Despite these limitations and areas of uncertainty, we tried to make our estimates of economic and disease burden as accurate as possible considering the limited availability of data. The most important product of this analysis is estimates of the aggregate and country-specific economic and disease burden of dengue in SEA. These estimates use a consistent methodology that allows comparison among countries and empirically derived adjustments for underreporting. The estimated burden of dengue would have been even higher had we considered other economic costs, such as prevention and vector control [18], [19], [85], [86], disruption of health systems due to seasonal clustering of dengue, decreases in tourism [87], long-term sequelae of dengue [80], [83], or disease complications associated to dengue infection [63], [64], [66], [88]–[92]. Even without counting these additions, our results suggest that exploring new approaches to reduce burden of dengue would be economically valuable. Supporting Information Figure S1 PRISMA 2009 Flow Diagram. Source: [48]. (TIF) Click here for additional data file. Table S1 PRISMA checklist for literature review. Note: As this manuscript is not a systematic review nor meta-analysis, the entries in the checklist are limited to those items applicable to this manuscript. Source: [48]. (DOCX) Click here for additional data file.
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                Author and article information

                Contributors
                Role: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: Formal analysis
                Role: ValidationRole: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                16 July 2018
                July 2018
                : 12
                : 7
                Affiliations
                [1 ] Epidemiology Unit, Institut Pasteur de la Guyane, Cayenne, French Guiana
                [2 ] National Reference Laboratory for Arboviruses, Institut Pasteur de la Guyane, Cayenne, French Guiana
                [3 ] Center for Global Health, Institut Pasteur de Paris, Paris, France
                VIET NAM
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Health Emergencies Programme, World Health Organization, Geneva, Switzerland

                Article
                PNTD-D-17-02024
                10.1371/journal.pntd.0006533
                6062120
                30011271
                © 2018 Fritzell et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 7, Tables: 2, Pages: 29
                Product
                Funding
                This study was supported by the 2014–2020 European Regional Development Fund under EPI-ARBO grant agreement (GY0008695). Camille Fritzell acknowledges funding from Calmette and Yersin allocated by the Pasteur Institut Department of International Affairs. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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