Infectious Research Articles

Social contacts patterns relevant to the transmission of infectious diseases in Suzhou, China following the COVID-19 epidemic

BackgroundThe COVID-19 pandemic has profoundly affected human social contact patterns, but there is limited understanding regarding the post-pandemic social contact patterns. Our objective is to quantitatively assess social contact patterns in Suzhou post-COVID-19.MethodsWe employed a diary design and conducted social contact surveys from June to October 2023, utilizing paper questionnaires. A generalized linear model was utilized to analyze the relationship between individual contacts and covariates. We examined the proportions of contact type, location, duration, and frequency. Additionally, age-related mixed matrices were established.ResultsThe participants reported an average of 11.51 (SD 5.96) contact numbers and a total of 19.78 (SD 20.94) contact numbers per day, respectively. The number of contacts was significantly associated with age, household size, and the type of week. Compared to the 0–9 age group, those in the 10–19 age group reported a higher number of contacts (IRR = 1.12, CI: 1.01–1.24), while participants aged 20 and older reported fewer (IRR range: 0.54–0.67). Larger households (5 or more) reported more contacts (IRR = 1.09, CI: 1.01–1.18) and fewer contacts were reported on weekends (IRR = 0.95, CI: 0.90–0.99). School had the highest proportion of contact durations exceeding 4 h (49.5%) and daily frequencies (90.4%), followed by home and workplace. The contact patterns exhibited clear age-assortative mixing, with Q indices of 0.27 and 0.28.ConclusionsWe assessed the characteristics of social contact patterns in Suzhou, which are essential for parameterizing models of infectious disease transmission. The high frequency and intensity of contacts among school-aged children should be given special attention, making school intervention policies a crucial component in controlling infectious disease transmission.

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning

BackgroundSepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis.MethodsClinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms.ResultsData from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322).ConclusionThe SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.

Launch of Board Certification in Pediatric Infectious Diseases in Japan.

To cultivate specialists in pediatric infectious diseases (ID) in Japan, the Japanese Society for Pediatric Infectious Diseases initiated board certification for pediatric ID in 2017. Previously, in 2014, we had formed a committee for board certification in pediatric ID and discussed the fundamentals of the board certification system, including the goals, requirements for designated training institutions, provisional certification of pediatric ID specialists and eligibility for and content of the board certification examination. After approval from 31 programs, the pediatric ID programs started in 2017 with 8 fellows in 7 programs. The first 6 graduates received board certification in 2020. To date, 61 pediatricians have been board certified as pediatric ID specialists. In parallel, we introduced board certification for pediatricians who work mainly in primary care settings and have a special interest in pediatric ID. This system has certified 338 pediatricians. During and after the development of the programs, we achieved substantial progress in highlighting the pivotal role of pediatric ID specialists, including the establishment and maintenance of antimicrobial stewardship programs, pediatric ID consultations and introduction of viral diagnosis by polymerase chain reaction at institutions. However, several issues need to be addressed, including the establishment of independent pediatric ID departments in institutions, payment of consultation fees, program site visits, maintenance of certification and cultivation of physician-scientists. These challenges will be the focus of future efforts.

Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.

FDA's proposed rule and its regulatory impact on emerging and reemerging neglected tropical diseases in the United States.

Diagnosing infectious diseases significantly influences patient care, aiding in outbreak identification, response, and public health monitoring. However, the range of FDA-approved molecular tests remains notably limited, especially concerning neglected tropical diseases (NTDs). Drawing upon our experience as one of the largest healthcare networks in the greater New York metropolitan area, this viewpoint manuscript aims to spotlight the existing diagnostic landscape and unmet clinical needs for 4 emerging NTDs increasingly prevalent in the United States, additionally, it delves into the possible adverse effects of the FDA's Proposed Rule on Laboratory-Developed Tests for these clinical conditions and the broader spectrum of NTDs.

Simple mathematical model for predicting COVID-19 outbreaks in Japan based on epidemic waves with a cyclical trend

BackgroundSeveral models have been used to predict outbreaks during the COVID-19 pandemic, with limited success. We developed a simple mathematical model to accurately predict future epidemic waves.MethodsWe used data from the Ministry of Health, Labour and Welfare of Japan for newly confirmed COVID-19 cases. COVID-19 case data were summarized as weekly data, and epidemic waves were visualized and identified. The periodicity of COVID-19 in each prefecture of Japan was confirmed using time-series analysis and the autocorrelation coefficient, which was used to investigate the longer-term pattern of COVID-19 cases. Outcomes using the autocorrelation coefficient were visualized via a correlogram to capture the periodicity of the data. An algorithm for a simple prediction model of the seventh COVID-19 wave in Japan comprised three steps. Step 1: machine learning techniques were used to depict the regression lines for each epidemic wave, denoting the “rising trend line”; Step 2: an exponential function with good fit was identified from data of rising straight lines up to the sixth wave, and the timing of the rise of the seventh wave and speed of its spread were calculated; Step 3: a logistic function was created using the values calculated in Step 2 as coefficients to predict the seventh wave. The accuracy of the model in predicting the seventh wave was confirmed using data up to the sixth wave.ResultsUp to March 31, 2023, the correlation coefficient value was approximately 0.5, indicating significant periodicity. The spread of COVID-19 in Japan was repeated in a cycle of approximately 140 days. Although there was a slight lag in the starting and peak times in our predicted seventh wave compared with the actual epidemic, our developed prediction model had a fairly high degree of accuracy.ConclusionOur newly developed prediction model based on the rising trend line could predict COVID-19 outbreaks up to a few months in advance with high accuracy. The findings of the present study warrant further investigation regarding application to emerging infectious diseases other than COVID-19 in which the epidemic wave has high periodicity.

Understanding the "Infodemic" Threat: A Case Study of the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic is notable among infectious diseases for its distinctive impact, which has halted millions of livelihoods owing to strict social distancing rules and lockdowns. Consequently, millions of individuals have turned to online sources, particularly social media, to remain informed about the virus. The transition to digital sources has resulted in an abundance of information, including both accurate and misleading or false content being shared and consumed on online platforms, contributing to what is commonly referred to as an "infodemic." Although these platforms have been valuable tools for healthcare professionals and public health authorities in disseminating crucial public health messages, they have also aided in the spread of misleading and false information. The widespread dissemination of false information has been instrumental in propagating harmful beliefs and behaviors such as vaccine hesitancy, promoting discriminatory attitudes, and endorsing false beliefs about the efficacy of certain therapeutic products for treating COVID-19. False information has undoubtedly become a challenge and burden for governments, health professionals, and the general population. This review has three main objectives: (1) to assess the scope of the "infodemic" issue, including investigating the factors contributing to the spread of false information online; (2) to examine the multifaceted consequences resulting from false information; and (3) to argue that an interdisciplinary, multi-layered approach, encompassing a focus on prevention, deterrence, and education, should be adopted to prevent the conception and dissemination of false information in this modern digital age.

Antimicrobial Activity, Genetic Relatedness, and Safety Assessment of Potential Probiotic Lactic Acid Bacteria Isolated from a Rearing Tank of Rotifers (Brachionus plicatilis) Used as Live Feed in Fish Larviculture

Aquaculture is a rapidly expanding agri-food industry that faces substantial economic losses due to infectious disease outbreaks, such as bacterial infections. These outbreaks cause disruptions and high mortalities at various stages of the rearing process, especially in the larval stages. Probiotic bacteria are emerging as promising and sustainable alternative or complementary strategies to vaccination and the use of antibiotics in aquaculture. In this study, potential probiotic candidates for larviculture were isolated from a rotifer-rearing tank used as the first live feed for turbot larvae. Two Lacticaseibacillus paracasei and two Lactiplantibacillus plantarum isolates were selected for further characterization due to their wide and strong antimicrobial activity against several ichthyopathogens, both Gram-positive and Gram-negative. An extensive in vitro safety assessment of these four isolates revealed the absence of harmful traits, such as acquired antimicrobial resistance and other virulence factors (i.e., hemolytic and gelatinase activities, bile salt deconjugation, and mucin degradation, as well as PCR detection of biogenic amine production). Moreover, Enterobacterial Repetitive Intergenic Consensus-PCR (ERIC-PCR) analyses unveiled their genetic relatedness, revealing two divergent clusters within each species. To our knowledge, this work reports for the first time the isolation and characterization of Lactic Acid Bacteria (LAB) with potential use as probiotics in aquaculture from rotifer-rearing tanks, which have the potential to optimize turbot larviculture and to introduce novel microbial management approaches for a sustainable aquaculture.

A nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome

Severe fever with thrombocytopenia syndrome (SFTS) is an acute infectious disease caused by a novel Bunyavirus infection with low population immunity and high mortality rate. Lacking specific therapies, the treatment measures vary with the severity of the disease, therefore, a case control study involved 394 SFTS patients was taken to determine risk factors for mortality. Comparative clinical data from the first 24 h after admission was collected through the electronic medical record system. Independent risk factors for death of SFTS were identified through univariate and multivariate binary logistic regression analyses. The results of the logistic regression were visualized using a nomogram which was created by downloading RMS package in the R program. In our study, four independent mortality risk factors were identified: advanced age(mean 70.45 ± 7.76 years), MODS, elevated APTT, and D-dimer. The AUC of the nomogram was 0.873 (0.832, 0.915), and the model passes the calibration test namely Unreliability test with P = 0.958, showing that the model's predictive ability is excellent. The nomogram to determine the risk of death in SFTS efficiently provide a basis for clinical decision-making for treatment.

Outbreak of Listeria monocytogenes in hospital linked to a fava bean product, Finland, 2015 to 2019.

Listeria monocytogenes (Lm) is a bacterium widely distributed in the environment. Listeriosis is a severe disease associated with high hospitalisation and mortality rates. In April 2019, listeriosis was diagnosed in two hospital patients in Finland. We conducted a descriptive study to identify the source of the infection and defined a case as a person with a laboratory-confirmed Lm serogroup IIa sequence type (ST) 37. Six cases with Lm ST 37 were notified to the Finnish Infectious Diseases Registry between 2015 and 2019. Patient interviews and hospital menus were used to target traceback investigation of the implicated foods. In 2021 and 2022, similar Lm ST 37 was detected from samples of a ready-to-eat plant-based food product including fava beans. Inspections by the manufacturer and the local food control authority indicated that the food products were contaminated with Lm after pasteurisation. Our investigation highlights the importance that companies producing plant-based food are subject to similar controls as those producing food of animal origin. Hospital menus can be a useful source of information that is not dependent on patient recall.

Immunity against measles, mumps, rubella, and varicella among homeless individuals in Germany — A nationwide multi-center cross-sectional study

IntroductionHomeless individuals suffer a high burden of vaccine-preventable infectious diseases. Moreover, they are particularly susceptible to adverse infection outcomes with limited access to the health care system. Data on the seroprevalence of measles, mumps, rubella, and varicella within this cohort are missing.MethodsThe seroprevalence of measles, mumps, rubella, and varicella was determined within the homeless population in Germany. Predictors of lacking immune protection were determined using multivariable logistic regression analysis.ResultsHomeless individuals in Germany (n = 611) showed a seroprevalence of 88.5% (95% CI: 85.8–91.0) for measles, 83.8% (95% CI: 80.6–86.6) for mumps, 86.1% (95% CI: 83.1–88.7) for rubella, and 95.7% (95% CI 93.8–97.2) for varicella. Measles seroprevalences declined from individuals born in 1965 to individuals born in 1993, with seroprevalences not compatible with a 95% threshold in individuals born after 1980. For mumps, seroprevalences declined from individuals born in 1950 to individuals born in 1984. Here, seroprevalences were not compatible with a 92% threshold for individuals born after 1975. Seronegativity for measles, mumps and rubella was associated with age but not with gender or country of origin.DiscussionHerd immunity for measles and mumps is not achieved in this homeless cohort, while there was sufficient immune protection for rubella and varicella. Declining immune protection rates in younger individuals warrant immunization campaigns also targeting marginalized groups such as homeless individuals. Given that herd immunity thresholds are not reached for individuals born after 1980 for measles, and after 1975 for mumps, vaccination campaigns should prioritize individuals within these age groups.

Antioxidant and antimicrobial activities of &lt;i&gt;Cassia alata&lt;/i&gt; (L.) Roxb. Leaves

Cassia alata is a plant of Caesalpiniaceae family used for the treatment of management infectious diseases. This work aimed at evaluating antioxidant and antimicrobial activities of hydroethanolic extract of Cassia alata leaves. The extract was obtained by the maceration of crude leaf powder (hydroethanolic 30:70). The phytochemical screening was focused on the detection of major chemical groups. The total flavonoids contents were studied using the aluminum chloride colorimetry method. The antioxidant capacity was carried out by the phosphomolybdate reduction method and by FRAP method. The antimicrobial activity of the extract was carried out by the diffusion method and the micro dilution. The extract showed the presence of phenolic compounds, flavonoids and reducing compounds. The concentration of total flavonoids of Cassia alata leaves was 70.91± 5.66 mg RE/g. The antioxidant activity by the phosphomolybdate reduction method and by the FRAP method were respectively 69.57 ± 4.83 mg AAE/g and 225.5 ± 17.32 mg FSE/g. The extract of Cassia alata has bactericidal action on Staphylococcus aureus ATCC 29213, Staphylococcus aureus SARM, Pseudomonas aeruginosa ATCC 27853 and Cutibacterium acnes ATCC 6919 and fungicidal action on Candida albicans ATCC 10231. This study shows that Cassia alata could be a source of a new antioxidant and antimicrobial agent.

Crosstalk between circadian clocks and pathogen niche.

Circadian rhythms are intrinsic 24-hour oscillations found in nearly all life forms. They orchestrate key physiological and behavioral processes, allowing anticipation and response to daily environmental changes. These rhythms manifest across entire organisms, in various organs, and through intricate molecular feedback loops that govern cellular oscillations. Recent studies describe circadian regulation of pathogens, including parasites, bacteria, viruses, and fungi, some of which have their own circadian rhythms while others are influenced by the rhythmic environment of hosts. Pathogens target specific tissues and organs within the host to optimize their replication. Diverse cellular compositions and the interplay among various cell types create unique microenvironments in different tissues, and distinctive organs have unique circadian biology. Hence, residing pathogens are exposed to cyclic conditions, which can profoundly impact host-pathogen interactions. This review explores the influence of circadian rhythms and mammalian tissue-specific interactions on the dynamics of pathogen-host relationships. Overall, this demonstrates the intricate interplay between the body's internal timekeeping system and its susceptibility to pathogens, which has implications for the future of infectious disease research and treatment.

Factors influencing uptake of protective behaviours by healthcare workers in England during the COVID-19 pandemic: A theory-based mixed-methods study.

Hospital infection control policies protect patients and healthcare workers (HCWs) and limit the spread of pathogens, but adherence to COVID-19 guidance varies. We examined hospital HCWs' enactment of social distancing and use of personal protective equipment (PPE) during the COVID-19 pandemic, factors influencing these behaviours, and acceptability and feasibility of strategies to increase social distancing. An online, cross-sectional survey (n = 86) and semi-structured interviews (n = 22) with HCWs in two English hospitals during the first wave of the COVID-19 pandemic (May-December 2020). The Capability, Opportunity, Motivation (COM-B) model of behaviour change underpinned survey and topic guide questions. Spearman Rho correlations examined associations between COM-B domains and behaviours. Interviews were analysed using inductive and deductive thematic analysis. Potential strategies to improve social distancing were selected using the Behaviour Change Wheel and discussed in a stakeholder workshop (n = 8 participants). Social distancing enactment was low, with 85% of participants reporting very frequently or always being in close contact with others in communal areas. PPE use was high (88% very frequently or always using PPE in typical working day). Social distancing was associated with Physical Opportunity (e.g., size of physical space), Psychological Capability (e.g., clarity of guidance), and Social Opportunity (e.g., support from managers). Use of PPE was associated with Psychological Capability (e.g., training), Physical Opportunity (e.g., availability), Social Opportunity (e.g., impact on interactions with patients), and Reflective Motivation (e.g., beliefs that PPE is effective). Local champions and team competition were viewed as feasible strategies to improve social distancing. It is valuable to understand and compare the drivers of individual protective behaviours; when faced with the same level of perceived threat, PPE use was high whereas social distancing was rarely enacted. Identified influences represent targets for intervention strategies in response to future infectious disease outbreaks.

Efficacy of a novel affitoxin targeting MOMP against Chlamydia trachomatis in vitro and in vivo

Abstract Targeted therapy is an attractive approach for treating infectious diseases. Affibody molecules have similar capability to antibodies that facilitate molecular recognition in both diagnostic and therapeutic applications. Targeting major outer membrane protein (MOMP) for treating infection of Chlamydia trachomatis, one of the most common sexually transmitted pathogens, is a promising therapeutic approach. Previously, we have reported a MOMP-specific affibody (ZMOMP:461) from phage display library. Here, we first fused it with modified Pseudomonas Exotoxin (PE38KDEL) and a cell-penetrating peptide (CPP) to develop an affitoxin, Z461X-CPP. We then verified the addition of both toxin and CPPs that did not affect the affinitive capability of ZMOMP:461 to MOMP. Upon uptake by C.trachomatis-infected cells, Z461X-CPP induced cell apoptosis in vitro. In animal model, Z461X significantly shortened the duration of C. trachomatis infection and prevented pathological damage in mouse reproductive system. These findings provide compelling evidence that the MOMP-specific affitoxin has great potential for targeting therapy of C. trachomatis infection.

MNGS helped diagnose scrub typhus-associated HLH in children: a report of two cases

BackgroundScrub typhus, caused by the Orientia tsutsugamushi (Ot), is a widespread vector-borne disease transmitted by chigger mites. Hemophagocytic lymphohistiocytosis (HLH) is considered to be one of the potentially severe complications. The diagnosis of scrub typhus-associated HLH may be overlooked due to the non-specific clinical characteristics and the absence of pathognomonic eschar.Case presentationWe obtained clinical data from two patients in the South of Sichuan, China. The first case involved a 6-year-old girl who exhibited an unexplained fever and was initially diagnosed with sepsis, HLH, and pulmonary infection. The other patient presented a more severe condition characterized by multiple organ dysfunction and was initially diagnosed with septic shock, sepsis, HLH, acute kidney injury (AKI), and pulmonary infection. At first, a specific examination for scrub typhus was not performed due to the absence of a characteristic eschar. Conventional peripheral blood cultures yielded negative results in both patients, and neither of them responded to routine antibiotics. Fortunately, the causative pathogen Orientia tsutsugamushi (Ot) was detected in the plasma samples of both patients using metagenomics next-generation sequencing (mNGS) and further confirmed by polymerase chain reaction. Subsequently, they both were treated with doxycycline and recovered quickly.ConclusionThe unbiased mNGS provided a clinically actionable diagnosis for an uncommon pathogen-associated infectious disease that had previously evaded conventional diagnostic approaches.

Soluble Plasma Proteins of Tumor Necrosis Factor and Immunoglobulin Superfamilies Reveal New Insights into Immune Regulation in People with HIV and Opioid Use Disorder

People with HIV (PWH) frequently suffer from Opioid (OP) Use Disorder (OUD). In an investigation of the impact of OUD on underlying immune dysfunction in PWH, we previously reported that OP use exacerbates inflammation in virally controlled PWH followed in the Infectious Diseases Elimination Act (IDEA) Syringe Services Program (SSP). Unexpectedly, Flu vaccination-induced antibody responses in groups with OUD were superior to PWH without OUD. Here, we investigated the profile of 48 plasma biomarkers comprised of TNF and Ig superfamily (SF) molecules known to impact interactions between T and B cells in 209 participants divided into four groups: (1) HIV+OP+, (2) HIV−OP+, (3) HIV+OP−, and (4) HIV−OP−. The differential expression of the top eight molecules ranked by median values in individual Groups 1–3 in comparison to Group 4 was highly significant. Both OP+ groups 1 and 2 had higher co-stimulatory TNF SF molecules, including 4-1BB, OX-40, CD40, CD30, and 4-1BBL, which were found to positively correlate with Flu Ab titers. In contrast, HIV+OP− exhibited a profile dominant in Ig SF molecules, including PDL-2, CTLA-4, and Perforin, with PDL-2 showing a negative correlation with Flu vaccine titers. These findings are relevant to vaccine development in the fields of HIV and OUD.

Urgent pandemic messaging of WHO, World Bank, and G20 is inconsistent with their evidence base

AbstractWhen international agencies make claims of an “existential threat” to humanity and advocate for urgent action from countries, it should be a safe assumption that they are consistent with their own data. However, a review of the data and evidentiary citations underlying the claims of the World Health Organization (WHO), the World Bank, and the Group of Twenty (G20) reveals a troubling picture in which the stated urgency and burden of infectious disease outbreaks, namely those of pandemic threat, is grossly misrepresented. These discrepancies in key documents and subsequent recitations in pandemic preparedness proposals have significant policy and financial implications. Disproportionate pandemic preparedness based on these false premises risks a significant opportunity cost through unnecessary diversion of financial and political resources away from global health priorities of higher burden. As WHO Member States plan to transform the way international health emergencies are managed at the World Health Assembly in May 2024, there is a crucial need to pause, rethink, and ensure future policy reflects evidence of need.

Association between ambient temperature and risk of notifiable infectious diseases in China from 2011 to 2019

ABSTRACT Previous studies on temperature and infectious diseases primarily focused on individual disease types, yielding inconsistent conclusions. This study collected monthly data on notifiable infectious disease cases and meteorological variables across 7 provinces in China from 2011 to 2019. A distributed lag nonlinear model was used to evaluate the association between ambient temperature and infectious diseases within each province, and random meta-analysis was applied to evaluate the pooled effect. Extreme hot temperature (the 97.5th percentile) was positively associated with the risk of respiratory infectious diseases with the relative risk (RR) of 1.45 (95%CI: 1.01–2.08). Conversely, extreme cold temperature (the 2.5th percentile) was negatively associated with intestinal infectious diseases and zoonotic diseases and vector-borne diseases, reporting RRs of 0.43 (95%CI: 0.30–0.60) and 0.46 (95%CI: 0.38–0.57), respectively. This study described the nonlinear association between ambient temperature and infectious diseases with different transmission routes, informing comprehensive prevention and control strategies for temperature-related infectious diseases.

Factors associated with infectious diseases fellowship academic success

ABSTRACT Background A multitude of factors are considered in an infectious disease (ID) training program’s meticulous selection process of ID fellows but their correlation to pre and in-fellowship academic success as well as post-fellowship academic success and short-term outcomes is poorly understood. Our goal was to investigate factors associated with subsequent academic success in fellowship as well as post-fellowship short-term outcomes. Methods In 2022, we retrospectively analyzed deidentified academic records from 39 graduates of the Mayo Clinic Rochester ID Fellowship Program (1 July 2013- 30 June 2022). Data abstracted included demographics, degrees, honor society membership, visa/citizenship status, medical school, residency training program, United States Medical Licensure Exam (USMLE) scores, letters of recommendation, in-training examination (ITE) scores, fellowship track, academic rank, career choice, number of honors, awards, and abstracts/publications prior to fellowship, during training, and within 2 years of graduation. Results Younger fellows had higher USMLE step 1 scores, pre and in-fellowship scholarly productivity, and higher ITE performance. Female fellows had significantly higher USMLE step 3 scores. Prior research experience translated to greater in-fellowship scholarly productivity. Higher USMLE scores were associated with higher ID ITE performance during multiple years of fellowship, but USMLE step 2 clinical knowledge and 3 scores were associated with higher pre and in-fellowship scholarly productivity and receiving an award during fellowship. The USMLE step 1 score did not correlate with fellowship performance beyond year 1 and 2 ITE scores. Conclusions Multiple aspects of a prospective fellow’s application must be considered as part of a holistic review process for fellowship selection. USMLE step 2 CK and 3 scores may predict fellowship performance across multiple domains.