Introduction: There is an extensive literature on public involvement (PI) in research, but this has focused primarily on experiences for researchers and public contributors and factors enabling or restricting successful involvement in specific projects. There has been less consideration of a ‘whole system’ approach to embedding PI across an organization from governance structures through to research projects.Objective: To investigate how a combination of two theoretical frameworks, one focused on mainstreaming and the other conceptualizing quality, can illuminate the embedding of positive and influential PI throughout a research organization.Methods: The study used data from the evaluation of a large UK research collaboration. Primary data were collected from 131 respondents (including Public Advisers, university, NHS and local government staff) via individual and group interviews/workshops. Secondary sources included monitoring data and internal documents.Findings: CLAHRC‐NWC made real progress in mainstreaming PI. An organizational vision and infrastructure to embed PI at all levels were created, and the number and range of opportunities increased; PI roles became more clearly defined and increasingly public contributors felt able to influence decisions. However, the aspiration to mainstream PI throughout the collaboration was not fully achieved: a lack of staff ‘buy‐in’ meant that in some areas, it was not experienced as positively or was absent.Conclusion: The two theoretical frameworks brought a novel perspective, facilitating the investigation of the quality of PI in structures and processes across the whole organization. We propose that combining these frameworks can assist the evaluation of PI research.

Background: The use of assisted reproductive technology (ART) is increasing worldwide, and observational studies have indicated that women who conceived by ART have an increased risk of pregnancy complications including gestational diabetes mellitus (GDM). We aimed to determine the risk of GDM among women who conceived with ART by systematic review and meta-analysis. Main text: A systematic literature search was conducted in ISI Web of Knowledge, MEDLINE, Scopus, and Embase through May 2017 for English-language articles using a list of keywords. All studies comparing GDM in women conceived by ART and those who conceived spontaneously were included. Data extraction was performed by two authors independently and discrepancies were resolved by discussion. In total, 48 studies with 91,487 pregnancies conceived through ART and 2,525,234 spontaneously conceived met the inclusion criteria. There was evidence of substantial heterogeneity among these studies (P < 0.001, I² = 98.6%). Random effects meta-analysis showed a significant increase in GDM among those who conceived by ART compared with those who conceived spontaneously (pooled relative risk = 1.51, 95% confidence interval = 1.18–1.93). Visual inspection of the funnel plot did not reveal any publication bias, which was supported by Egger’s test and Begg’s test. Conclusion: The findings of this systematic review indicate that the use of ART treatment is associated with a 1.51-fold increase in GDM. Women need to be counselled carefully before undergoing ART treatment about the possibility and risk of GDM.

A variety of factors are associated with greater COVID-19 morbidity or mortality, due to how these factors influence exposure to (in the case of morbidity) or severity of (in the case of mortality) COVID-19 infections. We use multiscale geographically weighted regression to study spatial variation in the factors associated with COVID-19 morbidity and mortality rates at the local authority level across England (UK). We investigate the period between March 2020 and March 2021, prior to the rollout of the COVID-19 vaccination program. We consider a variety of factors including demographic (e.g. age, gender, and ethnicity), health (e.g. rates of smoking, obesity, and diabetes), social (e.g. Index of Multiple Deprivation), and economic (e.g. the Gini coefficient and economic complexity index) factors that have previously been found to impact COVID-19 morbidity and mortality. The Index of Multiple Deprivation has a significant impact on COVID-19 cases and deaths in all local authorities, although the effect is the strongest in the south of England. Higher proportions of ethnic minorities are associated with higher levels of COVID-19 mortality, with the strongest effect being found in the west of England. There is again a similar pattern in terms of cases, but strongest in the north of the country. Other factors including age and gender are also found to have significant effects on COVID-19 morbidity and mortality, with differential spatial effects across the country. The results provide insights into how national and local policymakers can take account of localized factors to address spatial health inequalities and address future infectious disease pandemics.

Emigration of health workers has emerged as a significant obstacle in Iran, compelling policymakers to implement a diverse range of interventions and reforms to enhance healthcare services. It is imperative to comprehend the efficacy of emigration control interventions. To explore the intricate dynamics of elite emigration, this study employs a system dynamics modeling approach. The objective is to scrutinize Iranian health workers' emigration, examine the relationships, and evaluate the impact of various factors involved. The general purpose of this study is to analyze the policy interventions affecting the emigration of human resources in the field of health in Iran based on the system's dynamic approach. The research consists of four phases including analyzing the emigration status of health workers in developing countries, studying health personnel emigration in Iran, developing a System Dynamics (SD) model, and determining evidence-based policy interventions to address health worker emigration. These phases involve realist review, document analysis, qualitative interviews, data integration, and policy scenario planning. The primary objective is to gain a profound understanding of the underlying causes, mechanisms, and consequences of migration, as well as assess the impact of policies, in order to prioritize effective interventions. It seems that the SD model developed in this study can highlight the interconnectedness of various factors that influence health worker emigration, including demographic changes, economic conditions, and characteristics of healthcare systems. This study uses a systems dynamics approach to analyze health worker emigration from Iran, focusing on policies that promote retention and explore the implications of emigration on the healthcare system. By examining interrelationships and feedback loops within the healthcare system and socioeconomic factors, the study aims to identify effective policy interventions that can mitigate the negative effects of emigration.

Background: Markov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history.Methods: Data gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R²index as model fitting index.Results: Both Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model andp = 0.023 for Markov model). Finally, R²for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model).Conclusion: The MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures.