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There is no single model for Population Health Management (PHM) and different definitions have been proposed. All PHM models and definitions share the overall aim of improving population health and reduce healthcare costs. To achieve these objectives, PHM makes use of conceptual tools such as the Chronic Care Model and predictive medicine, and technical tools such as information systems and computational and record-linkage techniques to collect and analyse data. Using these tools, it makes it feasible to articulate PHM approaches in the following steps: identification of a population, stratification of individuals according to risk levels, mapping of health needs and development of targeted interventions and models of care. PHM has been applied in a variety of national and regional settings, proving to have great potential. However, the success of PHM models depends on a number of factors. In particular, few key points have emerged that must be taken into consideration when planning and implementing PHM programs. They include PHM funding schemes, strategies to ensure people adherence, the equity dimension in its multiple aspects, and the privacy of personal data. In addition to these challenges, there is the need to act in a legislative context appropriate to the implementation of PHM.

Background and Aim: As recently outlined in the WHO-ECDC Indicator framework (1) to evaluate the public health effectiveness of digital proximity tracing solutions, one of the main barriers to digital contact tracing (DCT) is population acceptance, which, in turns, is influenced by digital literacy, attitudes and practice. DCT came to public prominence during the COVID-19 pandemic but evidence on its population acceptance have not been comprehensively analyzed.  Methods: We carried out a systematic review (PROSPERO: CRD42021253668) following the PRISMA guidelines to collect, systematize and critically appraise the available evidence on population DCT acceptance.  Original studies reporting on different measures of population DCT acceptance were included., Conclusions: The systematic review was based on 41 articles meeting our a priori defined inclusion criteria, comprising aa total of 186144 surveyed subjects, 50000 tweets, 5025 Reddit posts and 714 written comments. Data extraction and synthesis required a qualitative outcome grouping, performed ex-post, in 14 different benchmarks components. They constitute a narrative analysis of actionable points for public health policy. Population acceptance is a key component of  DCT effective adoption and infection control during infectious diseases outbreaks. Assessing DCT acceptance's determinants in different settings, populations an cultural contexts it is of fundamental importance to inform the planning, implementation and monitoring of public health interventions. The results of our in-depth qualitative and quantitative analysis  will provide context for prospective improvements and actionable items and should guide future research aimed at exploring how digitalization can serve people-centred care.