The causal effects of mental health problems on the risk of infectious diseases remain vague. Investigating them via observational study is challenging as it presents possible confounding factors. Therefore, the objective of this study was to utilize Mendelian randomization (MR) techniques to evaluate the causal relationship between mental health problems and the risk of infectious diseases. Multivariable MR analyses were performed using genome-wide association data for sleep disorders (N = 216,700), depression (N = 500,199), anxiety (N = 290,361), nervous feelings (N = 450,700), unspecified mental disorder (N = 218,792), pneumonia (N = 486,484), skin and subcutaneous tissue infection (SSTI; N = 218,792), intestinal infectious diseases (IIDs; N = 218,792), urinary tract infection (N = 463,010), and central nervous system (CNS) infections (N = 218,792) among individuals of European ancestry. Independent genetic variants significantly (P < 10-8) associated with each exposure were considered instruments. The primary analysis used an inverse variance-weighted method, followed by a series of sensitivity analyses. Genetically predicted sleep disorders were associated with an increased risk of SSTI (odds ratio [OR], 1.29 [95% confidence interval (CI), 1.05-1.59]; P = .017). Genetically predicted depression was linked with an increased risk of CNS infections (OR, 1.59 [95% CI, 1.00-2.53]; P = .049) and SSTI (1.24 [95% CI, 1.03-1.49]; P = .024). Genetically predicted anxiety was associated with IIDs (OR, 1.19 [95% CI, 1.03-1.37]; P = .017) and SSTI (OR, 1.21 [95% CI, 1.02-1.43]; P = .029). There was no significant causal evidence for genetic prediction of nervous feelings and unspecified mental disorders in IIDs, CNS infections, SSTI, pneumonia, or urinary tract infection. Sensitivity analyses showed that the above causal association estimates were robust. In this MR study, we demonstrated a causal relationship between sleep disorders, depression, anxiety, and the risk of infectious diseases. However, no evidence was found to support causality between nervous feelings, unspecified mental disorders, and the risk of infectious diseases.

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach\'s alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran\'s health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to \"depict the epidemic curve during outbreaks of infectious diseases.\" Conversely, a notable weakness was the \"insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study\'s findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

Using simulated patients to mimic 9 established noncommunicable and infectious diseases, we assessed ChatGPT\'s performance in treatment recommendations for common diseases in low- and middle-income countries. ChatGPT had a high level of accuracy in both correct diagnoses (20/27, 74%) and medication prescriptions (22/27, 82%) but a concerning level of unnecessary or harmful medications (23/27, 85%) even with correct diagnoses. ChatGPT performed better in managing noncommunicable diseases than infectious ones. These results highlight the need for cautious AI integration in health care systems to ensure quality and safety.

BACKGROUND: Knowledge networks, such as Communities of Practice (CoP), are essential elements of knowledge management. They play a crucial role in assimilating various knowledge domains and converting individual knowledge into collective knowledge. This study aimed to assess the concept of knowledge networks and identify facilitators and barriers influencing knowledge sharing in infectious diseases, according to Iranian experts.
METHODS: This qualitative study employed content analysis and used purposive and snowball sampling. The data were collected via online or face-to-face interviews with 25 participants with diverse expertise in infectious diseases (both clinical and non-clinical), epidemiology, knowledge management, and knowledge-based business management in Iran. The thematic analysis technique was used to code the interviews, and the collected data were analyzed using MAXQDA 20 software.
RESULTS: Thematic analysis of the interviews led to 437 codes. These codes were categorized into two groups: facilitators and barriers. The facilitators shaping the knowledge network for infectious diseases were classified into three main categories: individual factors, organizational factors, and communication mechanisms. Individual factors involved two themes: strengthening knowledge exchange between experts in infectious diseases and personal characteristics such as the criteria for network membership. Organizational factors comprised three themes: organizational and trans-organizational factors, management strategies, and interactions with non-governmental sectors. Communication mechanisms included two themes: the use of information technology and knowledge brokers. In addition, three important challenges were identified as barriers influencing the knowledge network: administration and policy-making, organizational and trans-organizational, and personal challenges.
CONCLUSIONS: Several facilitators and barriers influence the formation of an infectious disease knowledge network, which must be addressed to ensure its effectiveness, development, and long-term sustainability. Addressing these factors will enable the network to effectively integrate diverse knowledge and contribute to advancing infectious disease management.

BACKGROUND: In sub-Saharan African countries, preventable and manageable diseases such as diarrhea and acute respiratory infections still claim the lives of children. Hence, this study aims to estimate the rate of change in the log expected number of days a child suffers from Diarrhea (NOD) and flu/common cold (NOF) among children aged 6 to 11 months at the baseline of the study.
METHODS: This study used secondary data which exhibit a longitudinal and multilevel structure. Based on the results of exploratory analysis, a multilevel zero-inflated Poisson regression model with a rate of change in the log expected NOD and NOF described by a quadratic trend was proposed to efficiently analyze both outcomes accounting for correlation between observations and individuals through random effects. Furthermore, residual plots were used to assess the goodness of fit of the model.
RESULTS: Considering subject and cluster-specific random effects, the results revealed a quadratic trend in the rate of change of the log expected NOD. Initially, low dose iron Micronutrient Powder (MNP) users exhibited a higher rate of change compared to non-users, but this trend reversed over time. Similarly, the log expected NOF decreased for children who used MNP and exclusively breastfed for six months, in comparison to their counterparts. In addition, the odds of not having flu decreased with each two-week increment for MNP users, as compared to non-MNP users. Furthermore, an increase in NOD resulted in an increase in the log expected NOF. Region and exclusive breastfeeding also have a significant relationships with both NOD and NOF.
CONCLUSIONS: The findings of this study underscore the importance of commencing analysis of data generated from a study with exploratory analysis. The study highlights the critical role of promoting EBF for the first six months and supporting children with additional food after six months to reduce the burden of infectious diseases.

BACKGROUND: The proportion of intense tropical cyclones is expected to increase in a changing climate. However, there is currently no consistent and comprehensive assessment of infectious disease risk following tropical cyclone exposure across countries and over decades. We aimed to explore the tropical cyclone-associated hospitalisation risks and burden for cause-specific infectious diseases on a multi-country scale.
METHODS: Hospitalisation records for infectious diseases were collected from six countries and territories (Canada, South Korea, New Zealand, Taiwan, Thailand, and Viet Nam) during various periods between 2000 and 2019. The days with tropical cyclone-associated maximum sustained windspeeds of 34 knots or higher derived from a parametric wind field model were considered as tropical cyclone exposure days. The association of monthly infectious diseases hospitalisations and tropical cyclone exposure days was first examined at location level using a distributed lag non-linear quasi-Poisson regression model, and then pooled using a random-effects meta-analysis. The tropical cyclone-attributable number and fraction of infectious disease hospitalisations were also calculated.
RESULTS: Overall, 2·2 million people who were hospitalised for infectious diseases in 179 locations that had at least one tropical cyclone exposure day in the six countries and territories were included in the analysis. The elevated hospitalisation risks for infectious diseases associated with tropical cyclones tended to dissipate 2 months after the tropical cyclone exposure. Overall, each additional tropical cyclone day was associated with a 9% (cumulative relative risk 1·09 [95% CI 1·05-1·14]) increase in hospitalisations for all-cause infectious diseases, 13% (1·13 [1·05-1·21]) for intestinal infectious diseases, 14% (1·14 [1·05-1·23]) for sepsis, and 22% (1·22 [1·03-1·46]) for dengue during the 2 months after a tropical cyclone. Associations of tropical cyclones with hospitalisations for tuberculosis and malaria were not significant. In total, 0·72% (95% CI 0·40-1·01) of the hospitalisations for all-cause infectious diseases, 0·33% (0·15-0·49) for intestinal infectious diseases, 1·31% (0·57-1·95) for sepsis, and 0·63% (0·10-1·04) for dengue were attributable to tropical cyclone exposures. The attributable burdens were higher among young populations (aged ≤19 years) and male individuals compared with their counterparts, especially for intestinal infectious diseases. The heterogeneous spatiotemporal pattern was further revealed at the country and territory level-tropical cyclone-attributable fractions showed a decreasing trend in South Korea during the study period but an increasing trend in Viet Nam, Taiwan, and New Zealand.
CONCLUSIONS: Tropical cyclones were associated with persistent elevated hospitalisation risks of infectious diseases (particularly sepsis and intestinal infectious diseases). Targeted interventions should be formulated for different populations, regions, and causes of infectious diseases based on evidence on tropical cyclone epidemiology to respond to the increasing risk and burden.
BACKGROUND: Australian Research Council, Australian National Health, and Medical Research Council.

The mixing groups gathered in the enclosed space form a complex contact network due to face-to-face interaction, which affects the status and role of different groups in social communication. The intricacies of epidemic spreading in mixing groups are intrinsically complicated. Multiple interactions and transmission add to the difficulties of understanding and forecasting the spread of infectious diseases in mixing groups. Despite the critical relevance of face-to-face interactions in real-world situations, there is a significant lack of comprehensive study addressing the unique issues of mixed groups, particularly those with complex face-to-face interactions. We introduce a novel model employing an agent-based approach to elucidate the nuances of face-to-face interactions within mixing groups. In this paper, we apply a susceptible-infected-susceptible process to mixing groups and integrate a temporal network within a specified time window to distinguish between individual movement patterns and epidemic spreading dynamics. Our findings highlight the significant impact of both the relative size of mixing groups and the groups\' mixing patterns on the trajectory of disease spread within the mixing groups. When group sizes differ significantly, high inter-group contact preference limits disease spread. However, if the minority reduces their intra-group preferences while the majority maintains high inter-group contact, disease spread increases. In balanced group sizes, high intra-group contact preferences can limit transmission, but asymmetrically reducing any group\'s intra-group preference can lead to increased spread.

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

Social determinants of health are known to underly excessive burden from infectious diseases. However, it is unclear if social determinants are strong enough drivers to cause repeated infectious disease clusters in the same location. When infectious diseases are known to co-occur, such as in the co-occurrence of HIV and TB, it is also unknown how much social determinants of health can shift or intensify the co-occurrence. We collected available data on COVID-19, HIV, influenza, and TB by county in the United States from 2019-2022. We applied the Kulldorff scan statistic to examine the relative risk of each disease by year depending on the data available. Additional analyses using the percent of the county that is below the US poverty level as a covariate were conducted to examine how much clustering is associated with poverty levels. There were three counties identified at the centers of clusters in both the adjusted and unadjusted analysis. In the poverty-adjusted analysis, we found a general shift of infectious disease burden from urban to rural clusters.

Discrimination and stigma are significant barriers to healthcare for the LGBTQIAPN+ community, necessitating a deeper analysis of their sociocultural causes. There is a notable gap in the literature regarding the understanding of socio-discursive representations and their impact on the stigmatization and pathologization of sexual minorities in the context of communicable diseases. This study aims to discuss the understanding of the sociodiscursive aspects of the health-disease process, particularly in stigmatized infectious diseases affecting the LGBTQIAPN+ community. The focus is on examining how news articles, or the set of analyzed texts (corpus), shape these perceptions. We conducted documentary research with a qualitative and discursive approach using news articles retrieved from Google NewsⓇ about diseases affecting the LGBTQIAPN+ population from 2011 to 2022. The analysis was based on critical discourse analysis, processed using MAXQDA and IRAMUTEQ software. The identified representations predominantly align with biomedical ideology, manifesting in a discourse that normalizes and medicalizes (normative-curative discourse), and notable for its pathologizing and stigmatizing nature. Six classes were found: Ethical professional dilemmas facing stigma, infection and contamination of the LGBT+ population, prejudice and discrimination in the form of information, stigma related to sexual behavior/orientation, Vulnerability and stigma related to infectious diseases, and strategies for minimizing health risk/stigma for the LGBT+ public. The most relevant analytical categories were related to infectious diseases and sexual identity. These themes were identified, indicating that media representations reinforce stigma and maintain unequal health practices (verticalization) for the LGBT+ community. Understanding these patterns within a broader historical context is crucial for promoting health education and strategies that challenge internalized prejudice. The need to reformulate cultural norms and develop health information and education policies is urgent. These policies should be led by professionals with a comprehensive and humanized vision, addressing the diverse needs of the LGBT+ population.