Limited resources in healthcare systems lead to the need to evaluate new health interventions. Economic evaluations provide methods to assess whether these interventions are more, equally, or less cost-effective. Based on the view that “Prevention is better than cure” [1], a larger focus could be placed on evaluating disease prevention and health promotion interventions rather than focusing on the treatment costs and effects of already emerged diseases. While prevention itself is not a new concept and has been extensively investigated, the field of digital public health (DiPH) is an expanding topic due to technological progress, increasing access, and reimbursement. There is also an emerging interest in the challenges of evaluating DiPH interventions [e.g. 2].
The potential benefits and advantages of digitalization in public health support the transition from cure to prevention, the empowerment of people and patients, and a more efficient, safer, and cheaper health care management delivery [3]. Established concepts of digitalization in healthcare and public health, such as e-health, m-health, and digital health, target the individual level. DiPH targets the population level [4]. DiPH also focuses on population, disease prevention, health promotion, and health inequalities [5]. In this review, the term digital is used in its broadest sense to refer to the use of information- and communication technology. Examples of digital applications are health apps, SMS reminders, web-based applications, and electronic devices.
Economic evaluations compare two or more interventions regarding costs and consequences [6]. Different types of economic evaluations can be distinguished. While cost-effectiveness analysis generally compares the relationship between costs and health outcomes, cost-utility analysis includes the concept of utilities. It generates combined outcomes by including, for example, disability or health-related quality of life combined with life years. Another type is cost-benefit analysis, which compares the willingness to pay for a specific intervention, or cost-consequences analysis, comparing costs during an assumed equal health effect. In the field of cost-effectiveness analysis or cost-utility analysis, a distinction can be made between study-based and model-based decision-analytic economic evaluation. Study-based economic evaluation often elicits data in a concrete trial over a shorter term than that of decision-analytic economic evaluations, which are based on decision analysis and combine different input parameters (e.g., based on single studies) using mathematical modeling over time.
There are advantages to decision-analytic economic evaluations. They enable the synthesizing of various input data and the inclusion of different comparators. They allow the extrapolation of costs and effects over time, and they enable the inclusion of available evidence of uncertainty in the specific decision problem [7]. Therefore, model-based economic evaluation can forecast costs and health outcomes over a long time. In contrast, a long-term randomized control trial of multiple complex interventions considering uncertainty would be time-consuming and expensive.
The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) is a guideline for improving economic evaluation reporting [8]. It provides a set of items concerning title and abstract, introduction, methods (e.g., comparator, time horizon, choice of model), results (e.g., study parameters or characterizing uncertainty), and the discussion of an economic evaluation. These items have been identified within a consensus process as the most important for the transparent reporting of this study type.
A distinction can be made between health promotion, primary prevention, and secondary prevention [9], whereby primary prevention includes actions taken to avoid the manifestation of a disease; secondary prevention aims to improve the chances for positive health outcomes through early detection (e.g., screening); and health promotion aims to empower people to increase their control over their health (e.g., decreasing behavioral risk in a population at increased risk). While the definition of DiPH is comprehensive, this review only focuses on health promotion and primary prevention to allow comparability of the methodologies and the results of a homogeneous group of studies. Based on the NICE Evidence Standards Framework for Digital Health Technologies [10], this study investigates preventive behavior change, including changes in user behavior related to health (e.g., smoking, alcohol). It excludes interventions used to treat diagnosed conditions. To make evidence-based decisions in DiPH, decision-makers and researchers need to know the available and already applied methods and models. Preventive interventions are connected with present costs and future benefits [11]. Therefore, decision-analytic/model-based economic evaluations are particularly suitable for synthesizing evidence and extrapolating future benefits and costs.
Overviews of specific fields of digital health already exist. They include preventive intervention and treatment of a condition or disease (e.g., diabetes [12] or depression [13]). While the economic evaluation of older people’s primary prevention and health promotion has already been investigated, there is a lack of evidence assessment regarding digital interventions in the broader population [14]. While the cost-effectiveness of internet-based interventions up to 2008 has been reviewed [15], the current review is updated and systematized. Recently, digital prevention and health promotion interventions have been investigated, but without a focus on cost-effectiveness [16].
This systematized review identifies and investigates the design and results of decision-analytic health economic evaluations associated with assessing the reporting quality in primary prevention and health promotion interventions that use digital tools.