Background: On March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus disease (COVID-19) a global pandemic. Starting in December 2019 from China, the first cases were officially announced on February 19 in Qom city, Iran. As of April 3, 2020, 206 countries have reported a total of 932166 cases with 46764 deaths. Along with China, USA, Italy, Spain, and Germany, Iran has been suffering the hardest burden of COVID-19 outbreak. Worse still, countries like Iran are struggling with the double burden of political sanctions to provide lifesaving medical equipment and medicines to combat the emergency. Methods: Using systematic document content analysis and through the lenses of health policy triangle, this article aims to compare the policies and strategies that Iran is adopting, with the experience and recommendations of China and WHO to combat COVID-19. Results: Iran has formulated contextual-based policies to combat COVID-19 outbreak before and after virus entrance. Insufficient whole-government, whole-society approach in managing the outbreak, inadequate lifesaving and protective equipment, and delayed decisive governance are the biggest challenges in policy making to combat COVID-19. COVID-19 policies are a public health concern and require professional advocacy attempts through appropriate inter-sectoral collaboration and whole-government coalitions. Conclusion: COVID-19 is an unfolding outbreak; hence, policy learning is crucial to formulate appropriate policies and implement them accordingly. Iran has made many efforts to defeat the outbreak, but more coherent, timely and efficient action is required, now, more than ever, to save lives and slow the spread of this pandemic.

A negative experience in childbirth is associated with chronic maternal morbidities. The aim of this systematic review and meta-analysis was to identify currently available successful interventions to create a positive perception of childbirth experience which can prevent psychological birth trauma. Randomized controlled trials of interventions in pregnancy or labour which aimed to improve childbirth experience versus usual care were identified from 1994 to September 2016. Low risk pregnant or childbearing women were chosen as the study population. PEDRO scale and Cochrane risk of bias tool were used for quality assessment. Pooled effect estimates were calculated when more than two studies had similar intervention. If it was not possible to include a study in the meta-analysis, its data were summarized narratively. After screening of 7832 titles/abstracts, 20 trials including 22,800 participants from 12 countries were included. Successful strategies to create a positive perception of childbirth experience were supporting women during birth (Risk Ratio = 1.35, 95% Confidence Interval: 1.07 to 1.71), intrapartum care with minimal intervention (Risk Ratio = 1.29, 95% Confidence Interval:1.15 to 1.45) and birth preparedness and readiness for complications (Mean Difference = 3.27, 95% Confidence Interval: 0.66 to 5.88). Most of the relaxation and pain relief strategies were not successful to create a positive birth experience (Mean Difference = − 2.64, 95% Confidence Intervention: − 6.80 to 1.52). The most effective strategies to create a positive birth experience are supporting women during birth, intrapartum care with minimal intervention and birth preparedness. This study might be helpful in clinical approaches and designing future studies about prevention of the negative and traumatic birth experiences.

Emerging Non-communicable diseases burden move United Nation to call for 25% reduction by 2025 in premature mortality from non-communicable diseases (NCDs)

Background: Given the potential of intersectionality to identify the causes of inequalities, there is a growing tendency toward applying it in the field of health. Nevertheless, the extent of the application of intersectionality in designing and implementing health interventions is unclear. Therefore, this study aimed to determine the extent to which previous studies have applied intersectionality and its principles in designing and implementing health interventions. Methods: The title and abstract of the articles which were published in different databases e.g. PubMed, Web of Science, Proquest, Embase, Scopus, Cochrane, and PsychInfo were screened. Those articles that met the screening criteria were reviewed in full text. The data about the application of principles of intersectionality, according to the stages heuristic model (problem identification, design & implementation, and evaluation), were extracted through a 38-item researcher-made checklist. Results: Initially, 2677 articles were found through reviewing the target databases. After removing the duplicated ones and screening the titles and abstracts of 1601 studies, 107 articles were selected to be reviewed in detail and 4 articles could meet the criteria. The most frequently considered intersectionality principles were “intersecting categories” and “power”, particularly at the stages of ‘problem identification’ as well as ‘design & implementation’. The results showed that “multilevel analysis” principle received less attention; most of the studies conducted the interventions at the micro level and did not aim at bringing about change at structural levels. There was a lack of clarity regarding the attention to some of the main items of principles such as “reflexivity” as well as “social justice and equity". These principles might have been implemented in the selected articles; however, the authors have not explicitly discussed them in their studies. Conclusions: Given the small number of included studies, there is still insufficient evidence within empirical studies to show the implication of intersectionality in designing and conducting health interventions. To operationalize the intersectionality, there is a need to address the principles at various stages of health policies and interventions. To this end, designing and availability of user-friendly tools may help researchers and health policymakers appropriately apply the intersectionality.

Background: Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI’s application and capabilities in health policymaking. Methods: We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords “artificial intelligence” and “policymaking.” We used Walt and Gilson’s policy triangle framework for charting the data. Results: The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts. Conclusion: Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI’s potential to foster the meaningful use of evidence-based policymaking.