Risk Factors For Influenza Research Articles

Clinical characteristics of severe influenza as a risk factor for febrile seizures in children: a retrospective analysis

ObjectiveTo retrospectively analyze the clinical characteristics and independent risk factors of severe influenza combined with febrile seizures, and to provide more basis for early clinical intervention.MethodsA total of 161 children with severe influenza were collected as study subjects and divided into convulsive (FC) group (40 cases) and non-convulsive (NFC) group (121 cases) according to whether they had febrile seizures. The demographic characteristics and clinical data of the two groups were analyzed. Multivariate logistic regression was used to analyze the risk factors of severe influenza combined with febrile seizures. The predictive efficacy was evaluated by receiver operating characteristic (ROC) curve analysis.Results(1) Multiple logistic regression analysis revealed that C-reactive protein (CRP) levels, Serum interleukin 6 (IL-6) levels, Days from onset of Flu symptoms to hospitalization, cerebrospinal fluid protein (CSF-TP) levels and the influenza virus type A (FluA) infection rate were found to be independent risk factors for severe influenza combined with febrile seizures in children. (2) ROC curve analysis showed that the cut-off value of CRP, Serum IL-6, Days from onset of Flu symptoms to hospitalization and CSF-TP were 7.57 mg/L, 9.84 pg/ml, 4.5 days and 194.8 mg/L, respectively.ConclusionChildren with severe influenza with CRP ≥ 7.57 mg/L, Serum IL-6 ≥ 9.84 pg/ml, Days from onset of Flu symptoms to hospitalization ≤4.5 days, CSF-TP ≥ 194.8 mg/L and FluA had a significantly increased risk of febrile seizures. It is useful for clinicians to determine the risk of severe influenza combined with febrile seizures, to adjust the early treatment plan, and to reduce the incidence of critically ill patients.

Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period

ObjectiveAt different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.MethodsDaily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day.ResultsIn periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B.ConclusionsThe varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

Relative Effectiveness of the MF59-Adjuvanted Influenza Vaccine Versus High-Dose Influenza Vaccine in Older Adults With Influenza Risk Factors During the 2019-2020 US Influenza Season.

This study estimated the relative vaccine effectiveness (rVE) of the MF59-adjuvanted trivalent influenza vaccine (aTIV) versus high-dose trivalent inactivated influenza vaccine (HD-TIV) for prevention of influenza-related medical encounters (IRMEs) during the 2019-2020 United States (US) influenza season stratified by the cumulative number of influenza risk factors. A secondary objective evaluated outpatient IRMEs and influenza- and pneumonia-related hospitalizations. This retrospective cohort study included US adults ≥65 years old vaccinated with aTIV or HD-TIV between 1 August 2019 and 31 January 2020. Electronic health records linked to claims were used to ascertain exposure, covariates, risk factors, and outcomes. Multivariable adjusted odds ratios (ORs) were derived using inverse probability of treatment-weighted samples to calculate rVEs independently for individuals with 0, ≥1, 1-2, or ≥3 risk factors. The study included 1 115 725 aTIV and 2 561 718 HD-TIV recipients. For the primary outcome of any IRME, the analysis found comparable effectiveness between aTIV and HD-TIV (rVE, 5.2% [95% confidence interval {CI}, -5.9% to 15.1%]) among those with 0 risk factors, whereas aTIV was more effective than HD-TIV among patients with ≥1, 1-2, or ≥3 risk factors (12.5% [95% CI, 10.0%-15.0%], 18.4% [95% CI, 13.7%-22.9%], and 10.4% [7.4%-13.3%], respectively). The same trends were observed for the secondary outcomes. This study demonstrated comparable effectiveness of aTIV and HD-TIV among individuals with no identified risk factors and higher effectiveness of aTIV compared with HD-TIV in preventing any IRMEs, outpatient IRMEs, and influenza- or pneumonia-related hospitalizations among those with at least 1 or multiple high-risk factors in adults ≥65 years old.

Genetic risk factors for COVID-19 and influenza are largely distinct

Coronavirus disease 2019 (COVID-19) and influenza are respiratory illnesses caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses, respectively. Both diseases share symptoms and clinical risk factors1, but the extent to which these conditions have a common genetic etiology is unknown. This is partly because host genetic risk factors are well characterized for COVID-19 but not for influenza, with the largest published genome-wide association studies for these conditions including >2 million individuals2 and about 1,000 individuals3–6, respectively. Shared genetic risk factors could point to targets to prevent or treat both infections. Through a genetic study of 18,334 cases with a positive test for influenza and 276,295 controls, we show that published COVID-19 risk variants are not associated with influenza. Furthermore, we discovered and replicated an association between influenza infection and noncoding variants in B3GALT5 and ST6GAL1, neither of which was associated with COVID-19. In vitro small interfering RNA knockdown of ST6GAL1—an enzyme that adds sialic acid to the cell surface, which is used for viral entry—reduced influenza infectivity by 57%. These results mirror the observation that variants that downregulate ACE2, the SARS-CoV-2 receptor, protect against COVID-19 (ref. 7). Collectively, these findings highlight downregulation of key cell surface receptors used for viral entry as treatment opportunities to prevent COVID-19 and influenza.

Risk factors associated with influenza A (H1N1)pdm09: a nested case control study of TB patients with ILI in Lahore District, Pakistan

BackgroundCo-morbidity with respiratory viruses including influenza A, cause varying degree of morbidity especially in TB patients compared to general population. This study estimates the risk factors associated with influenza A (H1N1)pdm09 in TB patients with ILI.MethodsA cohort of tuberculosis (TB) patients who were admitted to and enrolled in a TB Directly Observed Therapy Program (DOTs) in tertiary care hospitals of Lahore (Mayo Hospital and Infectious Disease Hospital) were followed for 12 weeks. At the start of study period, to record influenza-like illness (ILI), a symptom card was provided to all the participants. Every participant was contacted once a week, in person. When the symptoms were reported by the participant, a throat swab was taken for the detection of influenza A (H1N1)pdm09. A nested case control study was conducted and TB patients with ILI diagnosed with influenza A (H1N1)pdm09 by conventional RT-PCR were selected as cases, while those who tested negative by conventional RT-PCR were enrolled as controls. All cases and controls in the study were interviewed face-to-face in the local language. Epidemiological data about potential risk factors were collected on a predesigned questionnaire. Logistic analysis was conducted to identify associated risk factors in TB patients with ILI.ResultsFrom the main cohort of TB patients (n = 152) who were followed during the study period, 59 (39%) developed ILI symptoms; of them, 39 tested positive for influenza A (H1N1)pdm09, while 20 were detected negative for influenza A (H1N1)pdm09. In univariable analysis, four factors were identified as risk factors (p < 0.05). The final multivariable model identified one risk factor (sharing of towels, P = 0.008)) and one protective factor (wearing a face mask, p = < 0.001)) for influenza A (H1N1)pdm09 infection.ConclusionThe current study identified the risk factors of influenza A (H1N1)pdm09 infection among TB patients with ILI.

Predictors of complicated influenza infection in children presenting in a tertiary hospital in a tropical country: A case-control study

Influenza causes significant healthcare burden globally1 with highest risks in children and the elderly. In children, multiple studies have identified risk factors for severe influenza such as young age (&lt;5 years), presence of comorbidities, abnormal vital signs (e.g. hypoxia, shock) and bacterial coinfections.2-4 We found similar findings in our centre which included children during the influenza A (H1N1) 2009 pandemic with age &lt;2 years and comorbidity as risk factors for complicated influenza.5

Number of Influenza Risk Factors Informs an Adult's Increased Potential of Severe Influenza Outcomes: A Multiseason Cohort Study From 2015 to 2020.

While studies have evaluated factors influencing the risk of severe influenza outcomes, there is limited evidence on the additive impact of having multiple influenza risk factors and how this varies by age. Patients ≥18 years of age in the United States were evaluated retrospectively in 5 seasonal cohorts during the 2015-2020 influenza seasons. Patient-level electronic medical records linked to pharmacy and medical claims were used to ascertain covariates and outcomes. Multivariable logistic regression models were fitted for the overall population and by age subgroups to evaluate the association of demographic and clinical characteristics with odds of influenza-related medical encounters (ICD-10 codes J09*-J11*). The logistic regression models included sex, race/ethnicity, geographic region, baseline health care resource use, vaccination status, specific high-risk comorbidities, number of influenza risk factors, body mass index, and smoking status. Odds ratios from each of the 5 seasons were summarized via fixed effect meta-analysis. Season cohort sizes ranged from 887 260 to 3 628 168 adults. Of all patient characteristics evaluated, an individual's cumulative number of high-risk influenza conditions, as defined per the Centers for Disease Control and Prevention, was the most predictive of an increased probability of having an influenza-related medical encounter overall and across age groups. For adults of any age, odds ratios for influenza hospitalization ranged from 1.8 (95% CI, 1.7-2.0) for 1 risk factor to 6.4 (95% CI, 5.8-7.0) for ≥4 risk factors. These results show that a simple measure such as the number of influenza risk factors can be highly informative of an adult's potential for severe influenza outcomes.

Risk factors for avian influenza in Danish poultry and wild birds during the epidemic from June 2020 to May 2021.

Exploring the risk factors of avian influenza (AI) occurrence helps us to monitor and control the disease. Since late 2020, the number of avian influenza outbreaks in domestic and wild birds has increased in most European countries, including Denmark. This study was conducted to identify potential risk factors for wild birds and poultry during the epidemic in 2020/2021 in Denmark. Using Danish AI surveillance data of actively surveyed poultry and passively surveyed wild birds from June 2020 to May 2021, we calculated geographical attributes for bird locations and assessed the potential risk factors of AI detections using logistic regression analyses. 4% of actively surveyed poultry and 39% of passively surveyed wild birds were detected with AI circulating or ongoing at the time. Of these, 10 and 99% tested positive for the H5/H7 AI subtypes, respectively. Our analyses did not find any statistically significant risk factors for actively surveyed poultry within the dataset. For passively surveyed wild birds, bird species belonging to the Anseriformes order had a higher risk of being AI virus positive than five other taxonomic bird orders, and Galliformes were of higher risk than two other taxonomic bird orders. Besides, every 1 km increase in the distance to wetlands was associated with a 5.18% decrease in the risk of being AI positive (OR (odds ratio) 0.95, 95% CI 0.91, 0.99), when all other variables were kept constant. Overall, bird orders and distance to wetlands were associated with the occurrence of AI. The findings may provide targets for surveillance strategies using limited resources and assist in risk-based surveillance during epidemics.

Air Pollution and Influenza: A Systematic Review and Meta-Analysis.

Influenza is the first infectious disease that implements global monitoring and is one of the major public health issues in the world. Air pollutants have become an important global public health issue, in recent years, and much epidemiological and clinical evidence has shown that air pollutants are associated with respiratory diseases. We comprehensively searched articles published up to 15 November 2022 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Database of Chinese sci-tech periodicals, and Wanfang Database. The search strategies were based on keyword combinations related to influenza and air pollutants. The air pollutants included particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Meta-analysis was performed using the R programming language (R4.2.1). A total of 2926 records were identified and 1220 duplicates were excluded. Finally, 19 studies were included in the meta-analysis according to inclusion and exclusion criteria. We observed a significant association between partial air pollutants (PM2.5, NO2, PM10 and SO2) and the incidence risk of influenza. The RRs were 1.0221 (95% CI: 1.0093~1.0352), 1.0395 (95% CI: 1.0131~1.0666), 1.007 (95% CI: 1.0009~1.0132), and 1.0352 (95% CI. 1.0076~1.0635), respectively. However, there was no significant relationship between CO and O3 exposure and influenza, and the RRs were 1.2272 (95% CI: 0.9253~1.6275) and 1.0045 (95% CI: 0.9930~1.0160), respectively. Exposure to PM2.5, NO2, PM10, and SO2 was significantly associated with influenza, which may be risk factors for influenza. The association of CO and O3 with influenza needs further investigation.

Spatial modelling of infectious diseases with covariate measurement error

Abstract In spatial infectious disease models, it is typical to assume that only the distance between susceptible and infectious individuals is important for modelling, but not the actual spatial locations of the individuals. Recently introduced geographically-dependent individual level models (GD-ILMs) can be used to also consider the effect of spatial locations of individuals and the distance between susceptible and infectious individuals for determining the risk of infection. In these models, it is assumed that the covariates used to predict the occurrence of disease are measured accurately. However, there are many applications in which covariates are prone to measurement error. For instance, to study risk factors for influenza, people with low socio-economic status (SES) are known to be more at risk compared to the rest of the population. However, SES is prone to measurement error. In this paper, we propose a GD-ILM which accounts for measurement error in both individual-level and area-level covariates. A Monte Carlo expectation conditional maximisation algorithm is used for inference. We use models fitted to data to predict areas with high average infectivity rates. We evaluate the performance of the proposed approach through simulation studies and by a real-data application on influenza data in Manitoba, Canada.

2659. Substantially Elevated Influenza Risk in Vaccinated Immunocompromised Adults and High-Risk Patients Relative to All Vaccinated

Abstract Background Annual influenza vaccine is universally recommended for high-risk adults, but effectiveness varies between 19-60% in the general population by season and is markedly lower for specific high-risk groups, underscoring the need for novel prophylactic interventions. Though abundant in vaccine effectiveness studies, direct evidence quantifying influenza risk in vaccinated high-risk adults is limited. We aimed to estimate incidence of post-vaccination medically attended influenza-like Illness (ILI) and subsequent hospitalization among adults with chronic diseases and immunosuppression. Methods U.S. adults who received an influenza vaccine in influenza seasons 2015-2019 were identified using Optum commercial claims database (inpatient and outpatient). Known risk factors for influenza were assessed prior to vaccination, while medically attended ILI and subsequent hospitalization (within 30 days) were ascertained post-vaccination. Incidence rates and 95% confidence intervals (CI) were estimated for individual patient risk subgroup during the study period while maintaining sample independence across years. Relative risks were calculated using all vaccinated with or without risk as control. Results Among 5,195,160 vaccinated adults (median age 65; 73% with ≥1 comorbid condition), annual incidence of medically attended ILI was 2.04 [95% CI 2.03-2.06] per 100 person-years and 0.25 [0.25-0.26] per 100 person-years for ILI-related hospitalization. ILI-related hospitalization risk was the highest for immunocompromised transplant patients (5.6-13.9 relative risk ratio [RR] versus all vaccinated), late-stage kidney disease (RR: 5.4-7.4), and severe chronic lung disease (RR: 4.6-5.4), with likelihood of hospitalization after ILI up to 51.3%. More than half of ILI-related hospitalizations were in individuals with chronic obstructive pulmonary disorder, heart failure, or late-stage kidney disease. Conclusion Immunocompromised and individuals with severe chronic cardiopulmonary or renal disease retained the highest residual risk of ILI-related hospitalization despite vaccination. Additional prophylactic intervention complementing vaccination could potentially offer enhanced protection for these patients. Disclosures Natalia Zemlianskaia, PhD, Janssen Pharmaceuticals: Employee|Janssen Pharmaceuticals: Stocks/Bonds Amanda Zheutlin, PhD, Janssen Pharmaceuticals: Employee Yoshihiko Murata, MD, PhD, Gilead Sciences: Current employee of Gilead Sciences|Gilead Sciences: Stocks/Bonds|Merck Research Laboratories: Listed as co inventor in a patent|Merck Research Laboratories: Former employee of Merck Research Laboratories|ViiV Healthcare: Former employee of ViiV Healthcare Jyotsna Bhattacharya, MD, FAAP, Janssen Pharmaceuticals: Current Employee|Janssen Pharmaceuticals: Stocks/Bonds Geoffroy Coteur, PhD, Janssen Pharmaceutica: Honoraria|Janssen Pharmaceutica: Stocks/Bonds Gabriela Ispas, PhD, Janssen Pharmaceuticals: Current Employee|Janssen Pharmaceuticals: Stocks/Bonds Jorge Villacian, MD, Janssen Pharmaceuticals: Current Employee|Janssen Pharmaceuticals: Stocks/Bonds Jason Chien, MD, MS, Janssen Biopharma: Stocks/Bonds David Hong, MD, Janssen: Former employee|Janssen: Stocks/Bonds|Spero Therapeutics: Employee Jennings Xu, BA, Janssen Pharmaceuticals: Employee|Janssen Pharmaceuticals: Stocks/Bonds Breno Neri, PhD, Janssen R&amp;D: Full time employment|Janssen R&amp;D: Stocks/Bonds Khaled Sarsour, PhD, Janssen Pharmaceuticals: Employee|Janssen Pharmaceuticals: Stocks/Bonds Tripthi Kamath, PhD, Janssen Pharmaceuticals: Employee|Janssen Pharmaceuticals: Stocks/Bonds Xinggang lIU, MD, PhD, Janssen Pharmaceuticals: Current employee|Janssen Pharmaceuticals: Stocks/Bonds

Obesity exacerbates influenza-induced respiratory disease via the arachidonic acid-p38 MAPK pathway.

Obesity is a risk factor for severe influenza, and asthma exacerbations caused by respiratory viral infections. We investigated mechanisms that increase the severity of airway disease related to influenza in obesity using cells derived from obese and lean individuals, and in vitro and in vivo models. Primary human nasal epithelial cells (pHNECs) derived from obese compared with lean individuals developed increased inflammation and injury in response to influenza A virus (IAV). Obese mice infected with influenza developed increased airway inflammation, lung injury and elastance, but had a decreased interferon response, compared with lean mice. Lung arachidonic acid (AA) levels increased in obese mice infected with IAV; arachidonic acid increased inflammatory cytokines and injury markers in response to IAV in human bronchial epithelial (HBE) cells. Obesity in mice, and AA in HBE cells, increased activation of p38 MAPK signaling following IAV infection; inhibiting this pathway attenuated inflammation, injury and tissue elastance responses, and improved survival. In summary, obesity increases disease severity in response to influenza infection through activation of the p38 MAPK pathway in response to altered arachidonic acid signaling.

Epidemiological characteristics and analysis of related infection risk factors for influenza in Anhui Province from 2013 to 2021

Objective: Analysis of the characteristics of influenza epidemic in Anhui Province and quantification of the impact of different factors on influenza occurrence, providing scientific basis for better influenza prevention and control. Methods: Descriptive analysis and factor analysis were conducted on influenza-like illness (ILI) cases and RT-PCR results in Anhui Province from 2013 to 2021 using data from China's Influenza Monitoring Information System. Results: The percentage of influenza-like illness (ILI%) of sentinel hospitals in Anhui Province from April 1, 2013 to March 31, 2021 was 3.80% (1 209 142/31 779 987), showing an overall increasing trend, with a relatively high proportion in 2017-2018 at 4.30% (191 148/4 448 211). The proportion of ILI cases in infants and young children aged 0-4 years was a relatively high at 54.14% (654 676/1 209 142), and the highest ILI% was observed in Fuyang City, Anhui Province (6.25%, 236 863/3 788 863). Laboratory monitoring results showed that the positive rate of ILI cases in sentinel hospitals in 8 influenza monitoring years was 16.38% (34 868/212 912), showing an increasing trend year by year, with a relatively proportion in 2017-2018 at 26.19% (6 936/26 488). The detection rate of school-age children aged 5-14 years was a relativelyhigh at 28.81% (13 869/48 144), and the positive rate was a relatively high in Wuhu City among the 16 cities, reaching 22.01% (2 693/122 237). Influenza activity showed a single peak in winter-spring and alternating double peaks in winter-spring and summer, with different subtypes alternating, and A (H3N2) was the dominant subtype in summer. The results of a multiple logistic regression model showed that the positive rate was higher in 2017-2018, among children aged 5-14 years, in winter, and in southern Anhui. Conclusions: Influenza epidemic in Anhui Province has a clear seasonal pattern, and the ILI% and detection rate have shown an upward trend from 2013 to 2021. Therefore, it is suggested to ensure vaccine supply before the winter-spring influenza season arrives, and to strengthen vaccine uptake and health education to avoid the risk of infection during the peak period of influenza.

Annual trading patterns and risk factors of avian influenza A/H5 and A/H9 virus circulation in turkey birds (Meleagris gallopavo) at live bird markets in Dhaka city, Bangladesh.

The impacts of the avian influenza virus (AIV) on farmed poultry and wild birds affect human health, livelihoods, food security, and international trade. The movement patterns of turkey birds from farms to live bird markets (LBMs) and infection of AIV are poorly understood in Bangladesh. Thus, we conducted weekly longitudinal surveillance in LBMs to understand the trading patterns, temporal trends, and risk factors of AIV circulation in turkey birds. We sampled a total of 423 turkeys from two LBMs in Dhaka between May 2018 and September 2019. We tested the swab samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We used exploratory analysis to investigate trading patterns, annual cyclic trends of AIV and its subtypes, and a generalized estimating equation (GEE) logistic model to determine the factors that influence the infection of H5 and H9 in turkeys. Furthermore, we conducted an observational study and informal interviews with traders and vendors to record turkey trading patterns, demand, and supply and turkey handling practices in LBM. We found that all trade routes of turkey birds to northern Dhaka are unidirectional and originate from the northwestern and southern regions of Bangladesh. The number of trades from the source district to Dhaka depends on the turkey density. The median distance that turkey was traded from its source district to Dhaka was 188 km (Q1 = 165, Q3 = 210, IQR = 45.5). We observed seasonal variation in the median and average distance of turkey. The qualitative findings revealed that turkey farming initially became reasonably profitable in 2018 and at the beginning of 2019. However, the fall in demand and production in the middle of 2019 may be related to unstable market pricing, high feed costs, a shortfall of adequate marketing facilities, poor consumer knowledge, and a lack of advertising. The overall prevalence of AIV, H5, and H9 subtypes in turkeys was 31% (95% CI: 26.6-35.4), 16.3% (95% CI: 12.8-19.8), and 10.2% (95% CI: 7.3-13.1) respectively. None of the samples were positive for H7. The circulation of AIV and H9 across the annual cycle showed no seasonality, whereas the circulation of H5 showed significant seasonality. The GEE revealed that detection of AIV increases in retail vendor business (OR: 1.71; 95% CI: 1.12-2.62) and the bird's health status is sick (OR: 10.77; 95% CI: 4.31-26.94) or dead (OR: 11.33; 95% CI: 4.30-29.89). We also observed that winter season (OR: 5.83; 95% CI: 2.80-12.14) than summer season, dead birds (OR: 61.71; 95% CI: 25.78-147.75) and sick birds (OR 8.33; 95% CI: 3.36-20.64) compared to healthy birds has a higher risk of H5 infection in turkeys. This study revealed that the turkeys movements vary by time and season from the farm to the LBM. This surveillance indicated year-round circulation of AIV with H5 and H9 subtypes in turkey birds in LBMs. The seasonality and health condition of birds influence H5 infection in birds. The trading pattern of turkey may play a role in the transmission of AIV viruses in the birds. The selling of sick turkeys infected with H5 and H9 highlights the possibility of virus transmission to other species of birds sold at LBMs and to people.

Influenza and pneumococcal vaccinations and risk factors for pneumonia in older adults: A report by the Monitoring Report Committee of the Japanese Society of Public Health

Influenza and pneumococcal vaccinations and risk factors for pneumonia in older adults: A report by the Monitoring Report Committee of the Japanese Society of Public Health

Risk Factors for Influenza Disease in Shai-Osudoku and Ningo-Prampram Districts in the Greater Accra Region of Ghana

Aims: This study sought to determine the risk factors of Influenza by investigating the characteristics of patients with Acute Respiratory Infections (ARIs). Study Design: An unmatched case-control study. Place and Duration of Study: This was conducted among residents of Shai-Osudoku (SO) and Ningo-Prampram (NP) Districts from April to November 2016. Methods: Prospective cases of ARIs from a facility-based sentinel surveillance on respiratory illnesses were screened for Influenza-Like-Illnesses (ILIs). Respiratory samples were obtained from those meeting the case definition for ILI and investigated. A total of 147 samples were influenza virus-positive and selected as case-patients. Another 294 patients were selected as control-patients from the pool of influenza negative tests. These two groups were used to assess the risk factors of influenza. Crude and adjusted odds ratios were calculated. A purposive selection logistic regression was used for the adjusted modelling. Results: The study identified Influenza A(H3N2) and Influenza B as the predominant circulating influenza viruses. Study participants had poor knowledge on the causes and prevention of acute respiratory illness. After the multivariate analysis, ILI patients presenting with Chills had 4 times odds (aOR:4.57; 95%CI: 1.51 – 13.76) of having influenza as compared with the controls. In addition, ILI patients with recent travel history in past 2 weeks had 3 times odds (aOR:3.05; 95%CI: 1.07 – 8.73) of being infected with the influenza virus compared with controls with no history of travel. Conclusion: The study provided clues for increasing the index of suspicion of clinicians in identifying patients with respiratory signs who could be at risk of influenza infection. Communication of the significant risk factors identified in the study to all health workers should be prioritized as an action. This important knowledge about factors associated with influenza among ILIs will inform early detection and appropriate health interventions to reduce the burden influenza disease.

Prevalence, Seroprevalence and Risk Factors of Avian Influenza in Wild Bird Populations in Korea: A Systematic Review and Meta-Analysis.

Since the first recorded outbreak of the highly pathogenic avian influenza (HPAI) virus (H5N1) in South Korea in 2003, numerous sporadic outbreaks have occurred in South Korean duck and chicken farms, all of which have been attributed to avian influenza transmission from migratory wild birds. A thorough investigation of the prevalence and seroprevalence of avian influenza viruses (AIVs) in wild birds is critical for assessing the exposure risk and for directing strong and effective regulatory measures to counteract the spread of AIVs among wild birds, poultry, and humans. In this study, we performed a systematic review and meta-analysis, following the PRISMA guidelines, to generate a quantitative estimate of the prevalence and seroprevalence of AIVs in wild birds in South Korea. An extensive search of eligible studies was performed through electronic databases and 853 records were identified, of which, 49 fulfilled the inclusion criteria. The pooled prevalence and seroprevalence were estimated to be 1.57% (95% CI: 0.98, 2.51) and 15.91% (95% CI: 5.89, 36.38), respectively. The highest prevalence and seroprevalence rates were detected in the Anseriformes species, highlighting the critical role of this bird species in the dissemination of AIVs in South Korea. Furthermore, the results of the subgroup analysis also revealed that the AIV seroprevalence in wild birds varies depending on the detection rate, sample size, and sampling season. The findings of this study demonstrate the necessity of strengthening the surveillance for AIV in wild birds and implementing strong measures to curb the spread of AIV from wild birds to the poultry population.

Additional risk factors improve mortality prediction for patients hospitalized with influenza pneumonia: a retrospective, single-center case–control study

BackgroundSevere influenza, especially influenza pneumonia, causes large numbers of deaths each year. Some patients who develop severe influenza have no known risk factors. In this study we investigated risk factors for mortality of patients with influenza A-related pneumonia who have different basic conditions. We also evaluated the power of pneumonia severity assessment tools in Chinese patients hospitalized with influenza A-related pneumonia. Together, these results could provide a basis for a screening method that has improved ability for the early identification of critical patients who will have poor prognoses in clinical practice.MethodsThis single-center, retrospective case–control study included 152 adult patients with severe influenza over six influenza seasons. Data for diagnoses and demographics, as well clinical data, laboratory findings, treatment methods, 30-day and 60-day outcomes of the patients were collected. Patients who had any of the risk factors for severe influenza were included in the high-risk group, and those that had no known risk factors were included in the low-risk group.ResultsThe PSI, CURB-65 and PIRO-CAP tools all underestimated the mortality rate of patients hospitalized with influenza A-related pneumonia, and this underestimate was more pronounced for low-risk patients. D-dimer (Odds ratio [OR] = 1.052, 95% confidence interval [CI] 1.001–1.106, p = 0.045) and direct bilirubin (OR = 1.143, 95%CI 1.049–1.246, p = 0.002) were independent risk factors for mortality of patients with influenza A-related pneumonia. When used in combination with ferritin and D-dimer, the area under receiver operator characteristic curve (AUCROC) was 0.851 (95%CI 0.780–0.922, p < 0.001), 0.840 (95%CI 0.763–0.916, p < 0.001) and 0.829 (95%CI 0.748–0.911, p < 0.001) for PSI, CURB-65 and PIRO-CAP, respectively, which was higher than that obtained using PSI, CURB-65 and PIRO-CAP alone.ConclusionsThe findings demonstrate that currently used community-acquired pneumonia (CAP) scoring systems could underestimate the risk of influenza A-related pneumonia mortality. D-dimer was shown to be an independent risk factor of mortality for influenza A-related pneumonia in hospitalized patients, and a combination of D-dimer with ferritin could improve the predictive value of PSI, CURB-65 and PIRO-CAP for adverse prognoses of patients with influenza A-related pneumonia.

Influenza Prevalence and Risk Factors: A Population-Based Cross-Sectional Study

Influenza Prevalence and Risk Factors: A Population-Based Cross-Sectional Study

Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

BackgroundInfluenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention.MethodDistributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010–2018, 2010–2019, and 2010–2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions.ResultsThe cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005–1015 hPa, RHU > 60%, PRE was low, TEM was between 10–20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high.ConclusionAll eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.