Invest in Primary Health Care and Public Health for the Pandemic and Beyond:An ASPHER Statement

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Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics

The South Eastern Europe Health Network: A model for regional collaboration in public health

Inter-country alliances, articulated through regional approaches, have increasingly been used to drive economic development and social progress in the past several decades. The South Eastern Europe Health Network (SEEHN) stands out among these types of initiatives for the tangible improvements it has achieved in regional governance for health, with several important lessons for public health leaders worldwide. This review paper, written by several key participants in SEEHN operation, follows the main milestones in network development, including its foundation under the Stability Pact’s Initiative for Social Cohesion and the three ministerial forums that have shaped its evolution, in order to show how it can constitute a model for regional collaboration in public health. Herewith we summarise the main accomplishments of the network and highlight the keys to its success, drawing lessons that both international bodies and other regions may use in their own design of collaborative initiatives in health and in other areas of public policy

Characteristics of tobacco use among secondary school students: a cross-sectional study in a school in Valencia, Spain

IntroductionCigarette smoking is a significant public health problem, and it is essential to work actively with young people to limit the incorporation of this addiction. This study aimed to identify characteristics associated with tobacco use in adolescents in a real setting.MethodsEpidemiologic, cross-sectional study including secondary school students aged 12–17 years in the 1st, 2nd, and 3rd grades of “Joan Fuster High School” in the city of Sueca, Valencia (Spain). An anonymous, self-administered questionnaire was used to collect data on demographics, cigarette smoking history, alcohol consumption, nicotine dependence, and exposure to parental cigarette smoking.ResultsThe final sample of individuals surveyed included 306 students (50.6% females) with a median age of 13 years. The prevalence of cigarette smoking was 11.8% (13.5% in females and 9.9% in males). The mean age of cigarette smoking onset was 12.7 ± 1.6 years. Ninety-three students (30.4%) were repeaters, and 114 (37.3%) reported alcohol consumption. Significant factors associated with tobacco use were being a repeater (odds ratio [OR] 4.19, 95% confidence interval [CI] 1.75–10.55, p = 0.002), alcohol consumption (OR 4.06, 95% CI 1.75–10.15, p = 0.002) and parental cigarette smoking (OR 3.76, 95% CI 1.52–10.74, p = 0.007).DiscussionAn operational profile of features associated with tobacco consumption was identified in the presence of parental cigarette smoking, alcohol consumption, and poor academic performance. Consideration of these factors could be useful in the operational design of cigarette smoking cessation interventions for young people in a context where there is a great need for better prevention and control of cigarette smoking