An overview of Iran's actions in response to the COVID-19 pandemic and in building health system resilience.

This article is part of the Research Topic 'Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'. The considerable human, social, and economic impacts of COVID-19 have demonstrated a global lack of health system resilience, highlighting gaps in health system capacities due to fragmented approaches to health system financing, planning, and implementation. One of the key actions for ensuring equitable essential health services in all countries in normal situations as well as emergencies is through strengthening the primary healthcare (PHC) system. In the context of the unfolding pandemic, the Iranian Ministry of Health and Medical Education (MoHME) undertook a variety of strategic actions to ensure the sustainability of health services during the current health emergency and to promote health system resilience against future shocks. Right after the Alma-Ata declaration in 1978, MoHME pursued the PHC philosophy incorporating the principles within the WHO health system framework and its six building blocks. In response to the evolving pandemic, MoHME put in place several interventions to ensure the maintenance of essential health services in addition to the provision of response. Some interventions were new, informed by global experience with COVID-19, while others leveraged existing strengths within the existing health system. Those were taking a whole-of-government approach; leveraging the PHC capacity; supporting the workforce; strengthening preparedness and response; improving access to medicines, vaccines, and health products; and leveraging the health information system into the pandemic response. Health system strengthening that promotes resilience is imperative for governments as health systems are fundamental to sustainable socioeconomic development. In recognition of this, the WHO Eastern Mediterranean Regional Office (EMRO) has recently outlined regional priorities for advancing universal health coverage (UHC) and ensuring health security. Iran's approach both prior to and during the pandemic is strongly aligned with those regional priorities, which are "primary health care-oriented models; enhancing health workforce; promoting equity; enabling environment for research; improving access to countermeasures; and fostering health system resilience."

Detection of influenza epidemics using hidden Markov and Serfling approaches.

OBJECTIVE: Detection of epidemics is a critical issue in epidemiology of infectious diseases which enable healthcare system to better control it. This study is devoted to investigating the 5-year trend in influenza and severe acute respiratory infection cases in Iran. The epidemics were also detected using the hidden Markov model (HMM) and Serfling model. STUDY DESIGN: In this study, we used SARI data reported in the World Health Organization (WHO) FluNet web-based tool from August 2011 to August 2016. METHODS: SARI data in Iran from August 2011 to August 2016 were used. We applied the HMM and Serfling model for indicating the two epidemic and non-epidemic phases. The registered outbreak activity recorded on the WHO website was used as the gold standard. The coefficient of determination was reported to compare the goodness of fit of the models. RESULTS: Serfling models modified by 30% and 35% of the data had a sensitivity of 91.67% and 95.83%, while for 15%, 20% and 25% were 70.83%, 79.17% and 83.33%, respectively. Sensitivity of HMM and autoregressive HMM (AHMM) was 66.67% and 92.86%. All fitted models have a specificity of over 96%. The R2 for HMM and AHMM was calculated 0.73 and 0.85, respectively, showing better fitness of these models, while R2 was around 50% for different types of Serfling models. CONCLUSIONS: Both modified Serfling and HMM were acceptable models in determining the epidemic points for the detection of weekly SARI. The AHMM had better fitness, higher detection power and more accurate detection of the incidence of epidemics than Serfling model and high sensitivity and specificity. In addition to AHMM, Serfling models with 30% and 35% modification can be used to detect epidemics due to approximately the same accuracy but the simplicity of the calculations.

Enhancing surveillance for early detection of Zika virus infection: strategies for the countries of Eastern Mediterranean Region.

BACKGROUND: Zika virus infection (ZIKV) has caused major outbreaks in tropic and sub-tropic areas. No case from ZIKV has yet been reported in the countries of the Eastern Mediterranean Region (EMR) despite the presence of competent vector Aedes mosquitoes in many of these countries. AIMS: This study addresses appropriate surveillance strategies for early detection of ZIKV infection, which is important for EMR countries with established Aedes populations, but with no known or documented autochthonous transmission of ZIKV. METHODS: The WHO Regional Office for the Eastern Mediterranean developed a strategic framework for enhancing surveillance for ZIKV infection in EMR countries with established Aedes populations through a consultative process and review of available evidence. RESULTS: The framework calls for enhancing surveillance for early detection of ZIKV infection using a combination of both syndromic and event-based surveillance approaches. CONCLUSIONS: Enhancing surveillance for ZIKAV would require no shift in the existing system. A number of considerations would be required to integrate this syndromic and event-based surveillance approaches within the existing system.

Current trends in developing medical students' critical thinking abilities.

Health care is fallible and prone to diagnostic and management errors. The major categories of diagnostic errors include: (1) no-fault errors--the disease is present but not detected; (2) system errors--a diagnosis is delayed or missed because of the imperfection in the health care system; and (3) cognitive errors--a misdiagnosis from faulty data collection or interpretation, flawed reasoning, or incomplete knowledge. Approximately one third of patient problems are mismanaged because of diagnostic errors. Part of the solution lies in improving the diagnostic skills and critical thinking abilities of physicians as they progress through medical school and residency training. However, this task is challenging since both medical problem-solving and the learning environments are complex and not easily understood. There are many interacting variables including the motivation of the medical student (e.g. deep versus surface learning), the acquisition and evolution of declarative and conditional knowledge (e.g. reduced, dispersed, elaborated, scheme, and scripted), problem-solving strategies (e.g. procedural knowledge-guessing, hypothetical deductive, scheme inductive, and pattern recognition), curricular models (e.g. apprenticeship, discipline-based, body system-based, case-based, clinical presentation-based), teaching strategies (e.g. teaching general to specific or specific to general), the presented learning opportunities (PBL versus scheme inductive PBL), and the nature of the learning environment (e.g. modeling critical thinking and expert problem-solving). This paper elaborates on how novices differ from experts and how novices can be educated in a manner that enhances their level of expertise and diagnostic abilities as they progress through several years of medical training.