Dengue diversity across spatial and temporal scales: Local structure and the effect of host population size

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Abstract

A fundamental mystery for dengue and other infectious pathogens is how observed patterns of cases relate to actual chains of individual transmission events. These pathways are intimately tied to the mechanisms by which strains interact and compete across spatial scales. Phylogeographic methods have been used to characterize pathogen dispersal at global and regional scales but have yielded few insights into the local spatiotemporal structure of endemic transmission. Using geolocated genotype (800 cases) and serotype (17,291 cases) data, we show that in Bangkok, Thailand, 60% of dengue cases living <200 meters apart come from the same transmission chain, as opposed to 3% of cases separated by 1 to 5 kilometers. At distances <200 meters from a case (encompassing an average of 1300 people in Bangkok), the effective number of chains is 1.7. This number rises by a factor of 7 for each 10-fold increase in the population of the "enclosed" region. This trend is observed regardless of whether population density or area increases, though increases in density over 7000 people per square kilometer do not lead to additional chains. Within Thailand these chains quickly mix, and by the next dengue season viral lineages are no longer highly spatially structured within the country. In contrast, viral flow to neighboring countries is limited. These findings are consistent with local, density-dependent transmission and implicate densely populated communities as key sources of viral diversity, with home location the focal point of transmission. These findings have important implications for targeted vector control and active surveillance.

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Origins of dengue type 2 viruses associated with increased pathogenicity in the Americas.

R Rico-Hesse, L Harrison, R A Salas … (1997)

The recent emergence and spread of dengue hemorrhagic fever in the Americas have been a major source of concern. Efforts to control this disease are dependent on understanding the pathogenicity of dengue viruses and their transmission dynamics. Pathogenicity studies have been hampered by the lack of in vitro or in vivo models of severe dengue disease. Alternatively, molecular epidemiologic studies which associate certain dengue virus genetic types with severe dengue outbreaks may point to strains with increased pathogenicity. The comparison of nucleotide sequences (240 bp) from the E/NS1 gene region of the dengue virus genome has been shown to reflect evolutionary relationships and geographic origins of dengue virus strains. This approach was used to demonstrate an association between the introduction of two distinct genotypes of dengue type 2 virus and the appearance of dengue hemorrhagic fever in the Americas. Phylogenetic analyses suggest that these genotypes originated in Southeast Asia and that they displaced the native, American genotype in at least four countries. Vaccination and other control efforts should therefore be directed at decreasing the transmission of these "virulent" genotypes.

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Economic and Disease Burden of Dengue in Southeast Asia

Donald S. Shepard, Eduardo Undurraga, Yara A. Halasa (2013)

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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Spatial and Temporal Clustering of Dengue Virus Transmission in Thai Villages

Mammen Mammen, Chusak Pimgate, Constantianus Koenraadt … (2008)

Introduction Dengue is the leading cause of human arboviral disease worldwide. Dengue viruses (DENV) of the family Flaviviridae and genus Flavivirus, co-circulate as four antigenically related serotypes (DENV-1, −2, −3, and −4), each in varying annual frequencies in Thailand [1] and other tropical countries. The container-breeding mosquito Aedes aegypti (L.) serves as the primary vector responsible for DENV transmission within human populations. Females feed preferentially and frequently on human blood and consequently live in and around human dwellings [2,3]. Transmission of DENV to humans results in either inapparent infection, undifferentiated febrile illness, dengue fever (DF), or life-threatening dengue hemorrhagic fever (DHF). Except for a few notable exceptions, vector control (larvicide treatments, insecticide sprays, and source reduction) has been ineffectively implemented, and no vaccine or clinical cure is yet available for use. Consequently, DENV remain a major cause of morbidity in the tropics and threaten to further expand geographically. DENV transmission and disease are determined by a combination of factors [4] involving the human host [5–7], virus [8–11], mosquito vector [12,13], and environment [13]. Although past studies have revealed general temporal and spatial patterns in the distribution and abundance of Ae. aegypti and human DENV infections [14–18], greater resolution of transmission dynamics across finer geographic and temporal scales is needed to refine current dengue surveillance and control strategies. In an earlier prospective cohort study of schoolchildren in Thailand, Endy and others [19] reported a nonuniform distribution of DENV illness and viral serotypes. To test the hypothesis that DENV transmission is spatially and temporally focal, we extended the school-based study design to include cluster investigations [20] in villages associated with schools. By sampling children and mosquitoes within the neighborhood of children absent from school with fever and dengue viremia, we hypothesized that we would be able to detect, in the same general area and time, other human and mosquito infections and more precisely identify determinants of transmission risk. We used school-based dengue cases to trigger village surveillance of children and mosquitoes within spatial and temporal clusters. We sought a rigorous study of cluster areas over a 15-d period to more accurately define the burden of DENV within a prescribed area (both inapparent and symptomatic infections) and its relationship to mosquito density and infectivity. On the basis of our data, we aimed to consider implications on improving disease prevention strategies. Methods Study Area and Selection of Schools and Villages Our study area (Muang District, Kamphaeng Phet Province [KPP], north-central Thailand [19]) is, by Thai standards, relatively sparsely populated with 233,033 residents in 63,500 houses in an area encompassing 1,962 km2. The average temperature is 28.0 °C with an average monthly rainfall of approximately 200 mm during the rainy months of May to October (National Statistical Office). We selected 11 participating primary schools on the basis of higher numbers of hospitalized dengue cases amongst their students during the prior 5 y, proximity to our field station, and interest of the school administrators. Selected schools (Figure 1) were associated with 32 villages (8,445 houses). Given the workload limitations of entomological surveys, 20 of these villages (4,685 houses) were selected for inclusion on the basis of the density of houses, favoring those with houses in close proximity of each other ( 20–40 m, >40–60 m, >60–80 m, and >80–100 m). In order to evaluate for a distance effect in conjunction with enrollee demographics, a multivariate logistic regression model was formulated. Scientific and Ethical Review and Approval The study protocol and consent forms were approved by the AFRIMS Scientific Review Committee and the ethical review committees of the U.S. Army Surgeon General, Thai MoPH, University of California at Davis, University of Massachusetts Medical School, and San Diego State University. Results Initiation of Cluster Investigations Of the 1,204 febrile children (506 in 2004 and 698 in 2005) who provided blood specimens, 48 (28 in 2004 and 20 in 2005) had detectable DENV viremia. Thirty-four cluster investigations were conducted during the study period (Table 2). Ten clusters (five pairs) in 2004 and two clusters (one pair) in 2005 were spatially and temporally matched. The sex and age distribution of the positive and negative index cases were similar. Children in 58% (seven of 12) of the positive clusters (six in 2004 and one in 2005) attended a single school (school number 2). Table 2 Summary of Cluster Investigations Cluster Enrollees Among the 556 village enrollees (217 in positive and 339 in negative clusters), 27 DENV infections were detected during the 15-d follow-up period. These incident infections occurred exclusively in positive clusters (t-test; p < 0.01; AR = 10.4 per 100; 95% confidence interval [CI] 1–19.8 per 100). This result represented a 4.9% risk among enrollees for experiencing a DENV infection within 15 d of cluster initiation, but a 12.4% risk among enrollees who resided in a positive cluster. Cluster number 4 (Figure 2) contributed disproportionately to this difference. However, all but one positive cluster (cluster number 12) exhibited at least one neighbor with dengue within the 15-d period. There was a statistically significant clustering of DENV cases close to the center of positive clusters when we examined all positive clusters together (Figure 3). Demographics of enrollees between positive and negative clusters were comparable (Table 3). There was no difference in distance between the index cases and respective enrollees in the positive and the negative clusters. Table 3 Comparison of Dengue-Positive and Dengue-Negative Clusters Figure 2 Intense DENV Transmission in Cluster 4 Cluster number 4 illustrates extensive DENV transmission occurring within a 15-d period. In comparison, the paired negative cluster (cluster number 5, not shown) included 22 houses, 21 Ae. aegypti, and 15 contacts with no evidence of DENV transmission within a 15-d period. These index cases were 258 m apart and the cluster investigations were initiated 2 d apart. Figure 3 Clustering of DENV Infections within Positive Clusters This graph shows the relationship of distance between the houses of enrollees and the index case in the positive clusters and the proportion of those enrollees that experienced DENV seroconversion. Error bars represent 95% CIs of the proportions. Numbers in parenthesis indicate the number of positive enrollees and the total number of enrollees in each distance interval. The relationship between distance and the proportion of enrollees that are dengue positive was significant (Fisher's exact test, p < 0.001). A multivariate logistic regression model was estimated to examine the focal nature of transmission while controlling for cluster demographics. Distance between the house of each enrollee and the index case was the measure of focality. An indicator variable was used to account for the evidently excessive transmission in cluster number 4. The model included the age and gender of the enrollees as well as the interaction of these two variables. Resulting coefficient estimates, standard errors, and p-values are given in Table 4. A diagnostic test does not indicate a lack of fit (Hosmer-Lemeshow test, p = 0.23) [30]. A negative and significant parameter estimate indicated that the probability of infection decreased as the distance between enrollees and the index house increased. Modeling results also indicate a gender difference in the effect of age on the probability of infection. The probability that a male enrollee seroconverted decreased with age. This effect was not observed among female enrollees, in whom older enrollees had a higher probability of infection. These trends are apparent in the distribution of infections (Figure S1; Table 5). Table 4 Results of Multivariate Logistic Regression Analysis Table 5 Infections among Enrollees in Positive Clusters by Gender and Age Group Clustering was additionally observed within households as has been previously described [31]. Relative risk of dengue seroconversion among household enrollees of a dengue versus non-dengue case was 2.63 (95% CI 0.96–7.21; Pearson's Chi2 test) with an absolute risk of 6.88 per 100 (95% CI 0–17.29), indicating a strong, but not statistically significant trend towards household risk. Of the 27 DENV infections among village enrollees (Table 6), 14 were inapparent, and 13 were symptomatic. Inapparent infections were more likely with primary (five out of six) than secondary (seven out of 19) DENV infections (p = 0.05; Pearson's Chi2 test). All but one positive cluster (cluster number 6) had concordance of serotypes between the index case and viremic enrollees. (Pearson's Chi2 test used.) Table 6 Clinical Spectrum of Illness among 27 Enrollees with DENV Infections Environmental Determinants of Transmission Among environmental features evaluated ( Table 3), positive clusters were less likely to have piped water than were negative clusters. Though the number of water-holding containers was similar in houses with and without piped water (17.6 ± 8.6 versus 17.8 ± 8.1, t-test, p = 0.28), containers with Ae. aegypti larvae or pupae were significantly less abundant in houses with than without piped water (3.2 ± 3.0 versus 4.4 ± 3.3, t-test, p < 0.001). Use of the larvicide Temephos was higher in the schools than in the villages; 43% and 30% of containers had Temephos in schools in 2004 and 2005, respectively. On average 10% of containers had Temephos in the villages during both study years. Mosquito Collections and Spraying A total of 1,022 adult female Ae. aegypti were collected from within and immediately surrounding homes (Figure 1; Table 2) of which eight (0.8%) were PCR-positive. The average proportion of houses sampled was 0.92 in the positive clusters and 0.93 in the negative clusters (t-test, p = 0.53). Average number of Ae. aegypti pupae/person was significantly higher in positive clusters (Table 3). Although no significant differences were detected, all classical entomological indices (House, Container, and Breteau) and average number of female Ae. aegypti adults/person were higher in positive clusters. The average proportion of houses sprayed was 0.87 in the positive clusters and 0.84 in the negative clusters (t-test, p = 0.39). A total of eight female Ae. aegypti were collected from schools associated with cluster initiation; none were PCR-positive. Discussion Although focal DENV transmission has been noted previously [14,15,32], to our knowledge this is the first study to demonstrate, using control clusters and precise human and entomological data, recent DENV transmission that was focal through space and over a short time span (15 d). DENV-infected hosts (27 enrollees) and vectors (eight Ae. aegypti) were exclusively identified in the 12 dengue-positive clusters, despite a nearly 1:2 ratio of enrollees between positive and negative clusters. Furthermore, we observed significant central clustering of DENV cases within positive clusters. We suspect that focal transmission was associated with recent DENV introductions because of the 217 paired serologic specimens from positive cluster enrollees, only one revealed an elevated but declining immunoglobulin M level, which would be indicative of a recent DENV infection occurring up to 60 d prior to cluster initiation [22]. Consequently, we attributed the observed DENV transmission (enrollees with viremia on day 0 or 15 and/or seroconversion between days 0 and 15) to recent virus introductions. This conclusion is in contrast, however, to data published by Beckett and others [20] who conducted cluster investigations in West Jakarta, Indonesia. They detected 175 recent DENV infections upon enrollment in 53 positive clusters compared to our one in 12 positive clusters, arguing against recent virus introduction. We attribute these contrasting results to study design differences. First, we recruited from schools whereas Beckett recruited from a hospital, potentially after the virus had undergone significant community-based amplification. Second, we preferentially enrolled children as the primary susceptible and amplifying portion of the host population. Beckett additionally enrolled adults. Adults may have exhibited greater background dengue immunity that may have confounded the serologic data. Third, Beckett's study was conducted in an urban area, in contrast to rural villages in our study. Differences in transmission dynamics between these kinds of habitats were likely shaped by the frequency of DENV introductions and diversity in human behaviors. Previous studies have documented hyperendemicity of all four DENV serotypes with an approximate 5% annual risk of acquiring an infection in KPP [19]. In our study, cluster number 4 had a 52% attack rate among enrollees sampled during the 15-d follow-up period. However, after excluding this cluster and its matched negative cluster, the adjusted AR remained high (six per 100). This number represented a 12.4% risk of an enrolled child acquiring a DENV infection within a 15-d period when living within 100 m of a child ill with dengue. Eleven of 12 positive clusters had at least one enrollee with acute dengue in addition to the index case. Given the required intrinsic incubation period, and the finding that all eight virus isolates from mosquitoes matched the serotype recovered from the index case suggest, though not definitively, that except for children from whom virus was recovered on day 15, multiple viremic children within a cluster were infected by one or very few infected mosquitoes. Other evidence within our study to further support village- and not school-based vector sources of DENV infection are that: (1) mosquito populations in schools were extremely low, (2) children seroconverting to dengue within a cluster attended different classrooms within the school, (3) genomic sequences of the envelope (E)-regions of the viruses isolated from children and mosquitoes within the same villages were identical (R.G. Jarman, unpublished data), and (4) housemates of dengue seroconverters had a higher relative risk for DENV infection than those of nondengue seroconverters. The latter observation is consistent with previous reports [14–16]. We suspect that the predominance of DENV transmission in KPP villages reflects, at least in part, routine and effective vector control in schools (insecticide every May and July and Temephos to containers every 3 mo), but not in village homes. Differences in transmission observed between positive and negative clusters could not be attributed to differences in enrollee demographics. Differences in behavioral factors, however, could not be excluded. Within positive clusters, risk of infection decreased with age for males and increased with age for females. This observation merits further investigation with a larger sample and analysis of sex-specific behaviors that might modify risk of infection with advancing age. The only statistically significant determinant among environmental features associated with focal DENV transmission was the greater availability of piped water in negative clusters. Though one may consider a causal relationship (that is, less piped water availability leading to greater need for water storage leading to more containers for larval mosquito development resulting in higher dengue risk), we found no difference in the number of containers between cluster types. Although accurate data on water turn-over are difficult to obtain, the greater number of positive containers in positive than in negative clusters could not be explained by a difference in the frequency of container turn-over rates that we measured. These data could reflect a historical norm or behavior in response to lack of reliability of piped water possibly guided by people's knowledge of dengue preventive measures [33]. The only statistically significant difference among entomological indices was the greater number of Ae. aegypti pupae per person in positive than negative clusters. It is important to note that observed mean pupae per person exceed by an order of magnitude the minimum entomological threshold estimated by Focks and others [34] for a different region of Thailand. This implies that even when pupal densities are relatively high, differences in this measure of entomological risk can be epidemiologically informative. Although adult mosquito population density tended to be higher in positive clusters, differences were not statistically significant, perhaps due to limitations in sampling adult Ae. aegypti with backpack aspirators. Alternatively, mosquito density may be most informative when viewed in concert with herd immunity, and mosquito density alone may be less relevant than the presence of DENV-infected mosquitoes that potentially can transmit virus to multiple individuals [2,3]. Dengue cases in enrollees occurred over a wide range of female Ae. aegypti densities (Figure 4). At densities higher than approximately 1.5 Ae. aegypti females per child, clusters were more likely to be positive than negative. This indicates that DENV transmission was more likely to occur at higher vector densities. Figure 4 Relationship between Vector Density and Dengue Cases Relationship between the number of Ae. aegypti females per child and dengue transmission within 12 positive and 22 negative cluster investigations in 2004 and 2005. Dengue transmission is expressed as the number of positive PCRs on days 0 or 15 of study or of dengue seroconversions between days 0 and 15 per child per cluster. Perifocal spraying is a common approach by health departments to contain/control dengue. However, this practice has been found to be ineffective in aborting DENV transmission [13,35]. Our data suggest that if school-based surveillance can be bolstered by rapid, easy-to-use, and affordable diagnostics, spatially and temporally focused vector control in rural areas such as KPP could be more effectively applied to contain new virus introductions and offset the theoretical risk of longitudinal transmission within and beyond village foci. Although the risk of infection decreased significantly with distance from the center of a cluster, we did not examine people living beyond 100 m of an index case. Our study did not define the spatial dimensions of DENV transmission. Nevertheless, we expect that interventions will need to go beyond a 100 m radius of the home of a DENV-infected child because viremic residents or visitors bitten by an infected mosquito can move virus farther than a flying, infected adult female Ae. aegypti [13,35]. We do not know the longitudinal effects of killing adult mosquitoes on transmission within a community. Koenraadt and others [27] determined in our study area that within 1 wk of spraying insecticide inside homes, approximately 50% of prespraying levels of Ae. aegypti populations were reestablished. Identifying only two of 217 child enrollees with dengue viremia on day 15, both approximately 50 m from the index case within the same positive cluster, indicates that vector control can be locally successful when promptly and properly applied in response to a dengue case. Insecticide applications are most effective when applied inside homes where most Ae. aegypti rest [12] and otherwise avoid contact with insecticides applied outdoors [35–37]. Though our study design was rigorous, our conclusions must be considered in the context of largely logistical limitations: (1) We did not sample all children and mosquitoes within the cluster area. (2) We were unable to characterize the serotype of all DENV infections among village enrollees given restrictions in the frequency of collecting blood from children. (3) We did not collect data on human mobility/behavior that may have influenced the dynamics of transmission within the villages. (4) The possible contribution of adults to DENV transmission was not studied. (5) We did not study the seroprevalence profiles of cluster enrollees. Future studies should focus on positive clusters to more fully characterize the transmission dynamics, the impact of human behavior on transmission patterns, the appropriate spatial scale for disease surveillance/control, and identify more practical and cost-effective approaches to rapid dengue diagnosis. Our cluster methodology provided additional epidemiologic insights. Of note, 14 of the 27 cases of dengue among enrollees were clinically inapparent during this period when DENV-4 was the primary serotype circulating. Most (five of six) primary DENV infections detected in our study were clinically inapparent, similar to observations during a predominantly DENV-2 transmission year in Bangkok [38]. The nearly 1:1 ratio of inapparent to symptomatic secondary DENV infections in our study is also consistent with previous results from KPP [19]. DHF occurred in one (8%) of 12 symptomatic infections and one (4%) of 27 DENV infections confirming that severe dengue represents only a small fraction of the total DENV burden. Future cluster studies can complement these clinical and virologic data by examining correlates of protection that limit transmission, early immunologic events via postinoculation pre-illness specimens and their association with disease severity and sequence variation among viruses through time and space as they circulate between human and mosquito hosts. The prospective cluster methodology utilized here and by others [20] has the potential for broad application. It can be used for multidisciplinary transmission studies of other vector-borne viral diseases as well as spatially and temporally clustered infectious diseases. Supporting Information Figure S1 The Predicted Probability of Infection for Enrollees within Positive Clusters as a Function of Distance to the Index House The probabilities are given for males and females ages 3, 8, and 13 y. Model parameters are reported in Table 5. (51 KB DOC) Click here for additional data file.