Dengue presents a formidable and growing global economic and disease burden, with around half the world' s population estimated to be at risk of infection. There is wide variation and substantial uncertainty in current estimates of dengue disease burden and, consequently, on economic burden estimates. Dengue disease varies across time, geography and persons affected. Variations in the transmission of four different viruses and interactions among vector density and host' s immune status, age, pre-existing medical conditions, all contribute to the disease' s complexity. This systematic review aims to identify and examine estimates of dengue disease burden and costs, discuss major sources of uncertainty, and suggest next steps to improve estimates. Economic analysis of dengue is mainly concerned with costs of illness, particularly in estimating total episodes of symptomatic dengue. However, national dengue disease reporting systems show a great diversity in design and implementation, hindering accurate global estimates of dengue episodes and country comparisons. A combination of immediate, short-, and long-term strategies could substantially improve estimates of disease and, consequently, of economic burden of dengue. Suggestions for immediate implementation include refining analysis of currently available data to adjust reported episodes and expanding data collection in empirical studies, such as documenting the number of ambulatory visits before and after hospitalization and including breakdowns by age. Short-term recommendations include merging multiple data sources, such as cohort and surveillance data to evaluate the accuracy of reporting rates (by health sector, treatment, severity, etc.), and using covariates to extrapolate dengue incidence to locations with no or limited reporting. Long-term efforts aim at strengthening capacity to document dengue transmission using serological methods to systematically analyze and relate to epidemiologic data. As promising tools for diagnosis, vaccination, vector control, and treatment are being developed, these recommended steps should improve objective, systematic measures of dengue burden to strengthen health policy decisions.
Dengue presents a formidable global economic and disease burden with around half the world' s population estimated to be at risk of infection [1], [2]. Dengue transmission has intensified in the past decades, with outbreaks increasing in frequency, magnitude, and countries involved [3], [4]. Dengue disease varies across time and age of persons affected. This complexity results from the transmission of four different viruses affected by vector density, the host' s immune status, age, pre-existing medical conditions and other factors [5], [6]. The impact of dengue has been measured in terms of both monetary value and public health metrics, such as disability-adjusted life-years (DALYs) [7], [8]. Here we use the term “burden of dengue illness” to refer to the amount of clinically apparent disease and mortality imposed by dengue in a population. Economic burden has three main components: (i) costs of illness, estimated from the total symptomatic episodes multiplied by the average costs per episode [9], [10], (ii) costs of dengue prevention, surveillance, and control strategies [11], [12], and (iii) other impacts of dengue, usually harder to estimate, such as effects of dengue outbreaks on tourism [13], co-morbidities and complications associated with dengue virus (DENV) infection [14]–[16], or the effects of the seasonal clustering of dengue on health systems [17]. Accurate estimates of the economic and disease burden of dengue are critical to track health progress, assess program impact and results, and inform decisions about health policy, research, and health service priorities [7], [18]–[20]. However, estimates of dengue burden have substantial variability due to limitations in the availability, quality, and use of data. As promising technologies for vaccination, vector control, and disease management are being developed, more reliable measures of dengue illness burden are needed to produce better data on the economic cost of dengue. This systematic review aims to identify and examine estimates of dengue burden and their main sources of uncertainty and to develop an agenda for immediate, short-term, and long-term strategies to improve these estimates. Our main focus in this article is on the costs of illness, particularly from the challenges to estimate the total episodes of symptomatic DENV infections.
Available data on the economic and disease burden of dengue are limited. We conducted a systematic literature review of articles published or indexed in the Web of Science, MEDLINE, or in WHO' s Dengue Bulletin, combining the keyword “dengue” with the following list of keywords: surveillance, incidence, reporting, sensitivity, capture-recapture, cohort, economics, costs, burden, Aedes aegypti, and control. In addition, we added findings from previous literature reviews on dengue disease and economic burden [9], [10], [21]. For relevance to current dengue surveillance and management, we included articles published from 1995 through 2013 in English, Spanish, French, or Portuguese. The inclusion criteria for articles at each step of the review process (i. e. identification, screening, eligibility, and inclusion) are shown in the PRISMA flow diagram [22] (Figure 1). The review process left us with 88 articles. Our goal was not to obtain numerical findings from the individual studies, but rather to summarize the main strategies and data used to estimate the economic and disease burden of dengue and the sources of variability in the burden estimates.
Estimates of the disease and economic burden of dengue were derived by combining surveillance, clinical, and cost data. Since dengue is a reportable disease in many endemic countries, the incidence of dengue in a population can be estimated initially from cases reported to the surveillance system. But because surveillance systems are not designed to capture all episodes of symptomatic dengue, relatively low reporting rates lead to conservative incidence estimates [23]–[26]. Further, national dengue reporting systems show great diversity in design and implementation, and some developing countries have important resource limitations that hamper their ability to produce any systematic dengue-related data. Recent efforts to improve estimates of dengue burden include merging multiple data sources (e. g. , health and surveillance data, private laboratories, experts' opinion) [27], [28], analyzing the relationship between cohort studies and routine reporting [25], [26], [29], and estimating incidence and/or reporting rates using covariates (e. g. , healthcare access and quality, geographic and climate variables) [2], [8], [23]. To illustrate, there were about 2. 2 million reported episodes of dengue illnesses to WHO in 2010, but estimates of total symptomatic dengue incidence vary widely. Bhatt and others [2] estimated 96. 0 million dengue episodes globally. Their study combined a range of evidence of dengue transmission [1] with various sources of occurrence data (outbreak reports, cohort studies, online reporting, etc.), adjusting for the probability of occurrence of dengue based on socioeconomic, urban, and environmental covariates. Much of the dengue reporting occurs in areas of high transmission or during disease outbreaks, creating an upward trend in reports of dengue occurrence. Resulting models may have overstated total DENV infections. Also, the ratio of inapparent to apparent DENV infections varies substantially, depending on the age of patients, herd immunity and the circulating virus strain. The 2004 WHO global burden of disease (GBD) [30], estimated 9 million dengue episodes globally based on country-level datasets and information, and a systematic review of population-based incidence and mortality studies. By adjusting surveillance data with the rate of reporting of symptomatic DENV infections to health authorities, Shepard and others estimated about 30 million annual episodes treated in the medical system globally [31]. Last, Murray and others' GBD 2010 study [8] estimated global incidence of 0. 2 million dengue episodes in 2010. Noting that their approach underestimated disease burden for dengue and other neglected tropical diseases; improved updates for 2013 are underway [32]. A review of studies on the economic burden of dengue in 2011 highlighted the relatively sparse literature and conflicting results of existing cost studies [33]. A recent report proposed procedures for costing dengue illness [19]. Extensions to that document that may help in refining dengue economic evaluations include estimating unit costs that are sensitive to productivity loss for workers that are not part of the formal economy (e. g. , estimating the local marginal productivity of labor based on local wages averaged over the dengue season), examining local health-seeking behavior involving pharmacies or traditional healers, or using macro-costing techniques, which allow one to allocate overall operating costs among the outputs of a health facility [34] when person-level costs are unavailable. Estimates of health system congestion costs are also important; when health facilities are close to their capacity, the costs of an outbreak should also include costs that additional episodes impose on the system as a whole, like degradation of treatment quality of non-dengue patients [17]. Most important, improving current estimates of total dengue episodes is critical to quantify the disease and economic burden of dengue. Understanding the main sources of variability in the availability, quality, and use of reported data will allow for more comprehensive burden estimates. Consequently, through our literature review we identified the major sources of uncertainty, as a preliminary step in this direction. The number of limitations in reporting symptomatic dengue infections makes it difficult to estimate the true burden of dengue illness, which is probably underestimated in most studies. In this section, we suggest immediate, short-, and long-term refinements in data collection and analysis to improve the accuracy of estimations of the total dengue episodes and other components of disease burden. Table 1 lists the main sources of variability in dengue burden estimates and possible ways to improve data collection, including a few examples for some suggested improvements [6], [70], [91]–[94]. In the remainder of this section, we discuss possible analysis refinements with currently available data, or at least, data that could be gathered in the short run with marginal additional efforts.
Multiple factors contribute to the variability in estimates of dengue burden, making it challenging to obtain accurate estimates. We recommend a series of strategies for improving dengue-burden estimates; however, some of them may be costly and therefore harder to achieve, and strategies themselves may need to be evaluated for their cost-effectiveness. Possibly the most important limitation has to do with limited availability, quality, and use of dengue surveillance data in many countries. New prospective studies to ascertain dengue burden better are needed, particularly in areas where reporting is least complete (or nonexistent), such as Africa or South Asia. However, several improvements in economic and disease burden estimates may be achieved with available data. Reported surveillance data should include a narrative about the system' s main characteristics, including whether it includes the private sector, ambulatory episodes, cases of all ages, and type of lab confirmation, if any, of DENV infections reported. Most importantly, reporting to national surveillance systems should record each dengue episode as either hospitalized or ambulatory (i. e. , never hospitalized). The use of covariates to estimate the burden of dengue can adjust for underreporting and/or to extrapolate to areas where there is no reporting at all [2], [8], [23]. It would be important to characterize the context for epidemiological dengue studies to describe why these studies were conducted at the specific time and place, and how those settings compare to others in the country or region. Understanding how specific variables affect the burden of dengue will help researchers improve burden estimates. The greatest source of uncertainty in existing burden of dengue studies comes from underreporting of symptomatic DENV infections, followed by the type of treatment of episodes. Probabilistic sensitivity analyses and tornado diagrams are helpful to understand the proportion of a confidence interval that arises from various sources of uncertainty [10], [40]. The biggest payoff for burden of dengue estimates would come from studies that can link and analyze existing data. For example, data from cohort studies and clinical trials could be re-analyzed and compared with officially reported dengue episodes to estimate EFs [104] and population-based economic burden. Understanding the health-seeking behavior of people with symptomatic DENV infections would, for example, allow researchers to estimate the probability that a dengue episode is reported as a function of setting (inpatient or outpatient), sector (public or private), case severity, age, type of facility, access to healthcare, and other variables in the surveillance system. We also expect that neglected impacts of dengue, such as decreases in tourism or health system congestion, would represent substantial costs during outbreaks. We hope that future studies will obtain more accurate and comparable measures of economic and disease burden of dengue, for example, by documenting surveillance reporting criteria and adjustments used to estimate total symptomatic DENV infections (including adjustments for dengue episodes treated in the private sector or alternative health providers); using consistent case definitions; stratifying by treatment setting (hospitalized and non-hospitalized), severity, and age; using probabilistic sensitivity analysis to estimate uncertainty; and including comprehensive analysis of prevention and control costs. These improved estimates will be crucial for public health advisors and policy makers to identify optimal and cost-effective dengue control technologies and financing. Compared to other diseases with higher mortality rates or more frequent chronic symptoms, the DALY burden of dengue is relatively low; nevertheless, dengue poses a substantial burden on a large share of the world population. Estimates of dengue burden are sparse and there is significant room for refinement. Understanding the factors that shape the uncertainty around dengue burden and reporting will enable improvement of current estimates. Improving the methods to quantify dengue endemicity, for example, by using a measure of DENV incidence rather than disease, would also be a major improvement towards the goal of controlling dengue as it may allow more direct cross-country comparisons [93]. In the long run, we aim to identify the most cost-effective ways to control dengue, by combining various data sources and improving analytical tools. Costing studies can help us examine existing preventive and treatment approaches. Economic and epidemiological models can project costs and effectiveness of existing and alternative approaches in a range of settings. Most likely the future paradigm of dengue prevention and control will require an integration of vaccine, vector control, and anti-viral strategies, and systematic, comparable measures of dengue burden will be increasingly important. Several organizations have called for the improvement of health data [18]. We, too, believe this is an essential global public good that will help prioritize and improve public health decisions locally and globally.
Dengue is the most common mosquito-transmitted viral disease. It represents a formidable public health problem that is expanding in both infection rates and geographical range. Fortunately, vaccines, improved diagnostics, innovative vector control approaches, and other disease control methods are under development. Despite the importance of dengue, there is substantial uncertainty about the magnitude of the disease burden and economic cost of dengue, particularly in the number of symptomatic dengue infections. There is substantial variation in national reporting systems for dengue, which hinders accurate estimates of total cases and, therefore, of economic burden. Here we suggest a combination of immediate, short-term, and longer term strategies to address this knowledge gap. Immediate strategies include, for example, documenting the number of ambulatory visits before and after hospitalization. Short-term recommendations include merging multiple data sources, such as cohort and surveillance data to improve estimates of dengue incidence. Long-term efforts include increasing the collection and analysis of seroprevalence and economic data, use of enhanced surveillance (e. g. , use of incentives to improve reporting, include private sector sentinel sites). Implementing these steps would give policy makers more reliable, systematic data for strengthening and refining policies about the application and financing of new technologies to control dengue.
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Approaches to Refining Estimates of Global Burden and Economics of Dengue