The benefits of improving disease surveillance

The best recognised purpose of disease surveillance is the (early) detection of epidemics and other health threats. New diagnostic tools such as unbiased and targeted next-generation sequencing (NGS) are being explored as options to improve surveillance as these allow to determine causes of unexplained disease outbreaks, trace and link sources of disease transmission, and facilitate a better understanding of how viruses and bacteria pass from animal to humans. With NGS, the same platforms and sometimes even the same protocols can be used for the analysis of viruses, bacteria, genes and parasites with the potential for cost saving through economies of scale (economies of scale occur when the costs per unit decrease when produced quantities increase, as total costs can be divided among more units), improving its affordability also for lower income countries.3 4 As most emerging infectious diseases are zoonotic, further improvements could come from incorporating concepts from ‘One Health’, integrating capacities across human and animal health sectors. In general, sharing data in a timely manner, both within and between sectors and countries, is expected to greatly enhance and accelerate the understanding of diseases and their patterning and reduce unit costs. Improved collaboration and data sharing could further help to reduce barriers for the adaption of new technologies through reducing current human capital constraints. For instance, limited access to the specific expertise is an important barrier for the adoption of technologies such as NGS for various countries.5 A last example of how surveillance could be improved is by limiting current workforce constraints. This was evidenced during the COVID-19 pandemic where, for instance, testing capacities were falling short at some points. Indeed, many of such improvements could also improve the effectiveness and efficiency of healthcare systems in general.6 7

Incentives for research and data sharing

Currently, there are limited incentives and a lack of appropriate infrastructure for data sharing, which requires a clear governance structure that ensures a balance between privacy and access and adheres to (inter)national ethical and legal requirements. Solutions have been proposed for many of the issues limiting timely sharing of data in genomics research.5 An example of an initiative to enhance data sharing is the model for improving disease surveillance set by the Collaborative Management Platform for detection and Analyses of (Re-)emerging and foodborne outbreaks (COMPARE) and its sequel Versatile Emerging infectious disease Observator (VEO) (https://www.compare-europe.eu; https://www.compare-europe.eu/VEO). The project aims to develop a global platform for sharing and analysing NGS data that are customisable so groups of users can share data rapidly when needed while retaining ownership.3

Financing disease surveillance and the ‘global public good’ concept

Hossain and colleagues8 found that low-income and middle-income countries spend an annual median of $0.04 per capita on vaccine-preventable disease surveillance. The Organization for Economic Cooperation and Development provides estimates of spending on epidemiological surveillance and risk and disease control programmes for 2018 ranging from 0.002% (Luxembourg) to 0.9% (Switzerland) of total healthcare expenditures. Recent research on the willingness to pay for surveillance in the EU found that, on average, people are willing to spend €264/year, which roughly translates into 5% of total health spending.9 Based on this, it could be argued that within the EU currently too little is spent on disease surveillance.

An important characteristic of surveillance and interventions around infectious diseases is externalities, outcomes beyond the scope of those pursuing the activity. (That is, preventing the spread in one country may also prevent the spread to other countries. Although externalities might be an opportunity for collaboration, the risk is that actors consider only their own costs and benefits, leading to underprovision of surveillance activities). Public goods are a special case where externalities exist and goods are non-rivalrous and non-excludable. Infectious disease surveillance is sometimes described as a (global) public good (eg, people cannot be excluded from benefiting from a reduction in risk of infectious disease when its incidence is reduced (non-excludability), and one person benefiting from this reduction does not prevent anyone else from benefiting as well (non-rivalry)).10 Although the purity of the public good characteristics can be questioned,11 the concept can be useful to promote and justify public interference and funding. Non-excludability and thereby inability to demand payment eliminates commercial incentives for producing public goods in the private market. Consequently, the market fails to provide quantities optimal for society: an argument for governmental interference and financing. As no global government exists, global public goods are often financed by international organisations, national governments or transnational corporations.11 Such organisation of governmental and donor financing is a complex task, which requires attention for, among others, constraints related to fragmented financing within and across sectors and governmental levels.12

Investments in surveillance in developing countries are often initiated as development aid. From the global public good perspective, however, one could argue that financing by more developed countries is actually in those countries’ own interest. (One of the difficulties, however, is that countries typically have different priority diseases and thereby different surveillance priorities due to the various threats to different population groups. Where rich countries may fear the importation of new viruses, poor countries suffer from common infections which give rise to diarrhoea and respiratory diseases. Improved affordability of newer catch-all tools that can provide diagnosis for most common diseases and rule out emerging diseases would reduce these differences and stimulate international collaboration.) A recent example is the Access to COVID-19 Tools Accelerator (https://www.who.int/initiatives/act-accelerator), a global collaboration to produce and provide equitable access to tests, treatments and vaccines against COVID-19, which initially aids poorer countries though eventually helps to protect the entire world. Morton and colleagues addressed this perspective in a model based on which could be decided how development aid in global health should be spent.13 However, absence of hard evidence on cost-effectiveness of disease surveillance complicates the use of such analytical models.

The cost-effectiveness of disease surveillance

Methods of economic evaluation have been applied in cost-effectiveness of clinical interventions with a clear link between costs of an intervention and health benefits of the target patient group.14 Such evaluations are more complex in case of surveillance systems. In general, surveillance results in health improvements only when combined with other programmes as effective policy responses require a well-functioning health system and intragovernmental coordination. In case of healthcare emergencies, outbreaks further not only influence human health, health spending and labour market participation but also general security, animal health, and have broader disruptive effects on international trade and tourism. Furthermore, behavioural responses with respect to policies play a crucial role, making outcomes less predictable.15 Some issues regarding cost estimation are that costs from detected cases (including false positives) should be considered and that costs for the development of incentive structures for data sharing and facilitation of data sharing can be difficult to quantify and attribute to separate surveillance systems and activities. The latter relates to seeking an optimal balance between funding disease-specific (vertical) interventions and (horizontal) health system strengthening. Models were built (eg, previous works7 16) to address this, which take into account that investments in health systems will increase benefits of other programmes.

Another bottleneck is the absence of a methodological framework capturing the unique characteristics of surveillance. Earlier estimates of costs of surveillance and response activities were mainly restricted to predefined preparedness or response activities for endemic diseases and single pathogen models (see box 1 for an example).

Uncertainty is an important characteristic of infectious diseases that should be considered in evaluations, for instance, by using the ‘value of information’ and ‘real option’ concepts. Value of information focuses on uncertainty surrounding decisions and the role additional information can play in reducing uncertainty.17 In box 2, we explain this concept using an example. The example shows how information can aid policy makers to optimise outcomes, which we partly illustrate using a decision tree (figure 1) and simplified sensitivity analysis (figure 2), though also reveals other practical difficulties as how to get the industry to cooperate and how to divide the burden and consequences. Pooling of risks through insurance could be an option here. Although incentives for prevention and control in the market will be limited when it is known that potential losses will be reimbursed, such moral hazard is inherent to insurance and options to limit this, such as the deductible, can be applied. The real option approach focuses on the dynamic character of uncertainty by valuing the option to postpone an investment provided that some initial investment is made to ensure such options are available.18 This is important in the case of disease surveillance as appropriate timing of information provision and other actions is crucial.

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Figure 1

Decision tree.

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Figure 2

Sensitivity analysis.

Other issues in the context of economic evaluations of surveillance are the choice of the appropriate perspective and decision rules. (Two main perspectives used are the healthcare perspective (where only costs and benefits, the latter typically quantified in quality-adjusted life years (QALYs), within the healthcare sector are considered relevant) and the societal perspective (where all costs and benefits in society are considered).) A societal perspective appears to be most appropriate, recognising the broad impact of disease surveillance and facilitating comparisons across interventions (both in different sectors). Obtaining the value or decision rules for investments in surveillance is complex. In terms of the value of health, a recent review indicated an average value of a QALY of around €75 000.19 This figure only captures the value of health gains, and furthermore requires health gains to be specified in QALYs to be useful. The aforementioned study covering several countries within the EU into the valuation of an improved surveillance system indicated an average willingness to pay of around €22/month per household.9 Figures like this suggest that truly effective surveillance systems may well offer value for money from a societal perspective, given the broad range of benefits they potentially offer.