Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.

PURPOSE: To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases. METHODS: Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned. RESULTS: Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately. CONCLUSION: Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations. CLINICAL TRIAL REGISTRATION: Not applicable.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

COVID-19 pandemic: a health challenge for commoners during the first unlock phase in India.

Aim: COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. This disease has become a reason for disturbance and concern. India, as a densely populated country, took initiative after the pandemic was declared. The objective of this study was to determine the mortality and recovery rates at 30 days from the first unlock phase after five phases of lockdown. The the number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. Subject and methods: Statistical analysis was used to calculate the mortality rate, ratio between active and death cases, active cases and recovered cases, recovered and death cases in India during the first 30 days of the unlock phase. Results: The relationship between the new cases, deaths and recovered cases, shows that the new and recovered cases increased progressively. From the scatter plot of daily deaths and new cases, the R2 value is 0.0047. That means the death ratio is low against the new cases. Also, if we look at another scatter plot, the ratio between new cases and recovery rate shows the R2 value is 0.8015. That means the recovery rate was very high during the study period in India. The R2 value of daily recovery and death is 0.0072. India faced a huge number of new coronavirus cases and increased death rate every day during the first unlock phase. Conclusion: There was not the same condition as in the preliminary stage. The affected graphs progressively increased, and the government is fighting to control this deadly infection. Central and state governments are working together to combat this pandemic.

The race Against Time to Save Human Lives During the COVID-19 With Vaccines: Global Evidence.

Voluminous vaccine campaigns have been used globally, since the COVID-19 pandemic has brought devastating mortality and destructively unprecedented consequences to different aspects of economies. This study aimed to identify how the numbers of new deaths and new cases per million changed after half of the population had been vaccinated. This paper used actual pandemic consequence variables (death and infected rates) together with vaccination uptake rates from 127 countries to shed new light on the efficacy of COVID-19 vaccines. The 50% uptake rate was chosen as the threshold to estimate the real benefits of vaccination campaigns for reducing COVID-19 infection and death cases using the difference-in-differences (DiD) imputation estimator. In addition, a number of control variables, such as government interventions and people's mobility patterns during the pandemic, were also included in the study. The number of new deaths per million significantly decreased after half of the population was vaccinated, but the number of new cases did not change significantly. We found that the effects were more pronounced in Europe and North America than in other continents. Our results remain robust after using other proxies and testing the sensitivity of the vaccinated proportion. We show the causal evidence of significantly lower death rates in countries where half of the population is vaccinated globally. This paper expresses the importance of vaccine campaigns in saving human lives during the COVID-19 pandemic, and its results can be used to communicate the benefits of vaccines and to fight vaccine hesitancy.

Application of probabilistic models for extreme values to the COVID-2019 epidemic daily dataset.

Worldwide, COVID-19 coronavirus disease is spreading rapidly in a second and third wave of infections. In this context of increasing infections, it is critical to know the probability of a specific number of cases being reported. We collated data on new daily confirmed cases of COVID-19 breakouts in: Argentina, Brazil, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Mexico, Poland, Russia, Spain, U.K., and the United States, from the 20th of January, 2020 to 28th of August 2021. A selected sample of almost ten thousand data is used to validate the proposed models. Generalized Extreme-Value Distribution Type 1-Gumbel and Exponential (1, 2 parameters) models were introduced to analyze the probability of new daily confirmed cases. The data presented in this document for each country provide the daily probability of rate incidence. In addition, the frequencies of historical events expressed as a return period in days of the complete data set is provided.

Social distance capacity to control the COVID-19 pandemic: A systematic review on time series analysis.

BACKGROUND: Reducing interpersonal contact has been one of the least expensive and most widely used COVID-19 control strategies. OBJECTIVE: This systematic review has been conducted with the aim of identifying social distancing strategies and policies and their impact on the COVID-19 pandemic. METHODS: In order to compile this systematic review, Google Scholar, PubMed, Scopus, Web of Science, Science Direct, Magiran, SID, and Irandoc databases were searched from the COVID-19 outbreak until March 2021. Keywords included "social", "physical", "distance", "outbreak", "incidence", "prevalence", "spread", "new case", "death*", "mortality*", "morbidity*" , "covid-19", "coronavirus", "sars-cov-2" and "time series*". The articles were qualitatively evaluated by two researchers using the STROBE tool. Finally, the study data were divided into three conceptual categories by three researchers, who then agreed on one category. The practical suggestions were also categorized in the same way. RESULTS: The policies and strategies adopted to implement social distancing were included in five categories of restrictions, prohibitions, closures, incentives, and punishments. Transportation and travel restrictions, crowded places and schools closure, use of telecommunications and virtual communications, and financial and psychological support to society members were the main policies in this area. CONCLUSION: Rapid and complete vaccination of all people around the world is out of reach, therefore social distancing and the implementation of physical restraints, especially in crowded and densely populated environments, should be done extensively until COVID-19 is eradicated.

Novel cases of cutaneous phaeohyphomycosis by Alternaria alstromeriae, Epicoccum tritici and Phialemonium obovatum from North India.

BACKGROUND: A growing number of non-dermatophytic moulds and yeasts with the ability to act as human pathogens are reported every year. Dematiaceous fungi cause phaeohyphomycosis which encompasses a broad spectrum of diseases ranging from superficial (cutaneous and subcutaneous) to disseminated infections. Such fungal infections are responsible for causing significant morbidity and mortality, frequently in immunocompromised patients and rarely in immunocompetent patients. OBJECTIVES: To investigate the prevalence of cutaneous mycosis in Jammu district (India) and to isolate and identify the recovered causal agents from the affected skin of the patients. METHODS: For direct microscopy, 10% KOH was used. Skin samples were collected carefully from the affected areas of suspected patients, followed by the isolation and identification of the causal agents by cultural examination, morphological examination and ITS sequencing. RESULTS: Herein, we report and describe three new cases of cutaneous phaeohyphomycosis from District Jammu of Union Territory Jammu and Kashmir, India. The age of the patients under study ranged from 17 to 42 years and the duration of infection from 1 to 2 years. The etiological agents that were recovered from the patients under study were Alternaria alstromeriae, Epicoccum tritici and Phialemonium obovatum. These dematiaceous fungal species were isolated from the skin specimen of immunocompetent hosts. CONCLUSION: Among the three isolated etiological agents, two (Alternaria alstromeriae, Epicoccum tritici) represent new global records and one (Phialemonium obovatum) new record to India as causal agents of cutaneous phaeohyphomycosis. Careful microscopic and mycological examination form the basis of correct diagnosis of such fungal infections in the absence of simple and reliable laboratory tests (serologic or antigen tests).

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Positive Correlation Between General Public Knowledge and Attitudes Regarding COVID-19 Outbreak 1 Month After First Cases Reported in Indonesia.

The increasing number cases of coronavirus disease (COVID-19) infections in the general population in Indonesia raises questions concerning the public's knowledge and attitudes regarding this pandemic. To determine the correlation between the general public's knowledge and attitudes regarding the COVID-19 outbreak 1 month after the first cases were reported in Indonesia. This cross-sectional study was conducted between early March and the end of April 2020 in the general population of Indonesia, beginning with the North Sumatra region, where the spread of COVID-19 in Indonesia began. Questionnaires were randomly distributed online in the red zone in Indonesia. Data were collected by collecting people's responses to the questionnaire, which were distributed via WhatsApp (WA) application and were competed independently by the participants. A descriptive analysis was conducted to describe the demographic characteristics, knowledge, and attitudes of the general population. A total of 201 people had good knowledge (98%) and a positive attitude (96%) regarding the COVID-19 pandemic. The respondents had a negative attitude in relation to two aspects of the COVID-19 outbreak: having to always maintain a distance of 1.5 m when in crowds, and not being able to regularly exercise or eat nutritious food (78.6% and 79.1%, respectively). Most people in Indonesia have good knowledge and a positive attitude regarding the COVID-19 pandemic. However, negative attitudes were still found in this study, and as a result, transmission prevention measures cannot reach their maximum effectiveness by simply publicizing the increase in day-to-day cases to the general public.

Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.

BACKGROUND: The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. OBJECTIVE: This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. METHODS: Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). RESULTS: Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. CONCLUSIONS: Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Analyzing the effect of duration on the daily new cases of COVID-19 infections and deaths using bivariate Poisson regression: a marginal conditional approach.

The whole world is devastated by the impact of the COVID-19 pandemic. The socioeconomic and other effects of COVID-19 on people are visible in all echelons of society. The main goal of countries is to stop the spreading of this pandemic by reducing the COVID-19 related new cases and deaths. In this paper, we analyzed the correlated count outcomes, daily new cases, and fatalities, and assessed the impact of some covariates by adopting a generalized bivariate Poisson model. There are different effects of duration on new cases and deaths in different countries. Also, the regional variation found to be different, and population density has a significant impact on outcomes.

Tuberculosis in Poland in 2018.

AIM OF THE STUDY: To evaluate the main features of tuberculosis (TB) epidemiology in 2018 in Poland and to compare with the situation in the EU/EEA countries. METHODS: Analysis of case- based data on TB patients from National TB Register, data on anti-TB drug susceptibility testing results in cases notified in 2018, data from National Institute of Public Health- National Institute of Hygiene on HIV-positive subjects for whom TB was an AIDS-defining disease, data from Central Statistical Office on deaths from tuberculosis based on death certificates, data from the report " European Centre for Disease Prevention and Control/WHO Regional Office for Europe. Tuberculosis surveillance and monitoring in Europe 2020- 2018 data. Stockholm: European Centre for Disease Prevention and Control, 2020". RESULTS: In 2018, 5487 TB cases were reported in Poland. The incidence rate was 14.3 cases per 100000, with large variability between voivodeships from 7.3 to 23.4 per 100 000. The mean annual decrease of TB incidence in 2014- 2018 was 3.8%. In 2018, 4852 cases were newly diagnosed with no history of previous treatment i.e. 12.6 per 100 000. 635 cases i.e. 1.7 per 100 000 - 11.6% of all registered subjects were previously treated for tuberculosis. In 2018, the number of all pulmonary tuberculosis cases was 5224 i.e. 13.7 per 100000. Pulmonary cases represented 95.2% of all TB cases. In 2018, 243 extrapulmonary TB cases were found i.e. 0.6 per 100 000. In the whole country there were 52 pediatric cases of tuberculosis. TB in children represented 0.9% of all cases notified in Poland in 2018. The incidence rates of tuberculosis were growing along with the age group from 0.9 per 100 000 among children to 24.7 per 100 000 among subjects in the age group 45-64 years (the highest incidence rate). In 2018, the incidence rate in the age group ≥65 years was 21.3 per 100 000. The TB incidence among men i.e. 21.0 per 100 000 was 2.6 times higher than among women i.e. 8.0 per 100 000. The biggest difference in the TB incidence between the two sex groups occurred in persons aged 55 to 59 years - 44.9 vs. 9.8 and in age group 60- 64 years - 43.7 vs. 10.2. The TB incidence in rural population was lower than in urban, respectively 13.4 per 100 000 and 14.9 per 100 000. The number of all registered culture positive TB cases was 4075. Pulmonary tuberculosis was bacteriologically confirmed in 3935 subjects. Cases confirmed by culture represented 74.3% of all TB cases and 75.3% of all pulmonary TB cases. The number of smear-positive pulmonary TB cases reported in 2018 was 2324 i.e. 6.1 per 100 000 accounting for 44.3% of all pulmonary TB cases and 59.1% of pulmonary TB cases confirmed by culture. In all patients with tuberculosis in Poland in 2018 there were 48 cases with MDR-TB (among them 14 foreigners) and 83 patients with resistance to isoniazid only, representing respectively 1.3% and 2.2% of cases with known DST results (DSTs were available in 90.7% of all culture-confirmed TB cases). In 2018, there were 97 patients of foreign origin among all cases of tuberculosis in Poland. TB was AIDS-indicative disease in 14 subjects with HIV co-infection. There were 490 deaths due to tuberculosis reported in 2017 - 1.3 per 100 000; 468 people died from pulmonary and 22 from extrapulmonary tuberculosis. Mortality among males - 2.1 per 100 000 - was 3.6 X higher than among females - 0.5. 40.2% of all TB deaths were cases 65 years old and older - 3.1 per 100 000. In 2017, there was no death from tuberculosis in children and no deaths in adolescents. In 2017, tuberculosis represented 0.1% of total mortality in Poland and 25.4% of mortality from infectious and parasitic diseases. CONCLUSIONS: In 2018, the incidence of tuberculosis in Poland was lower than in 2017. Despite a continuous decline it is still higher than the average in the EU/EEA countries. The highest incidence rates were observed in older age groups. The participation of pediatric cases is smaller than average in the EU/EEA countries. The incidence in males was more than 2 times higher than in females. The impact of migration on the characteristics of tuberculosis in Poland is not substantial. In Poland, MDR-TB is less common than the average in the EU/EEA countries.

COVID-19: update of the Italian situation.

On the March 11, 2020, the World Health Organization (WHO) declared the novel coronavirus disease 2019 (COVID-19) outbreak as a pandemic. The first cases in Italy were reported on January 30, 2020, and quickly the number of cases escalated. On March 20, 2020, according to the Italian National Institute of Health (ISS) and National Institute of Statistics (ISTAT), the peak of COVID-19 cases reported in Italy reached the highest number, surpassing those in China. The Italian government endorsed progressively restrictive measures initially at the local level, and finally, at the national level with a lockdown of the entire Italian territory up to 3 May 2020. The complete Italian territory closing slowed down the contagion. This review retraces the main numbers of the pandemic in Italy. Although in decline, the new reported cases remain high in the northern regions. Since drugs or vaccines are still not available, the described framework highlights the importance of the containment measures to be able to quickly identify all the potential transmission hotspots and keep control subsequent epidemic waves of COVID-19.

ARIMA modelling and forecasting of irregularly patterned COVID-19 outbreaks using Japanese and South Korean data.

The World Health Organization (WHO) upgraded the status of the coronavirus disease 2019 (COVID-19) outbreak from epidemic to global pandemic on March 11, 2020. Various mathematical and statistical models have been proposed to predict the spread of COVID-2019 [1]. We collated data on daily new confirmed cases of the COVID-19 outbreaks in Japan and South Korea from January 20, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARIMA) model were introduced to analyze two data sets and predict the daily new confirmed cases for the 7-day period from April 27, 2020 to May 3, 2020. Also, the forecasting results and both data sets are provided.

An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases.

As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments' interventions. We studied the periods before and after the dates of major governments' interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries" [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.

New cases are they incident or newly discovered / 中华疾病控制杂志

@#New case is a basic concept in epidemiology and often serves as the numerator for the indexes of the frequency of disease in a population． However，the exact definition of new case is still un- clear． Based on the natural history of disease combined with the concept of event-state，this article rede- fines case-related concepts and compares them with traditional concepts． The study found that the so- called new cases in traditional epidemiological studies are actually the cases firstly discovered，including newly discovered cases and identifiable newly incident cases ( disease initiation) ． In the real world，new cases are often difficult to measure，and together with the continuous process of disease development and iceberg phenomenon，those may affect the accuracy of disease frequency measurement and causal infer- ence． A correct understanding of the meaning of case-related concepts，the meanings they reflect，and the differences between them help to rationally interpret the results of epidemiological research．

Explore the change trend fitting prediction model on new cases of occupational diseases in Guangdong Province / 中国职业医学

OBJECTIVE: To screen the optimal fitting model for the change trend of the number of new cases of occupational diseases in Guangdong Province by using linear and nonlinear regression models. Method The number of new cases of occupational diseases in Guangdong Province from 2003 to 2017 was used as the dependent variable(■) and the year(time) as the independent variable(x).Eleven mathematical models including linear regression, cubic function, quadratic function, composite function, growth function, exponential function, logistic function, power function, logarithmic function, S-type function and inverse function were used to fit the data, and the best-fit model was selected to describe and verify the change of new occupational diseases. RESULTS: Among the 11 mathematical models, the determination coefficient of fit results of cubic curve regression model was the highest(0.94, P<0.01), and the fit effect was the best. The fitting curve was ■. The cubic curve regression model was used to fit the number of new cases of occupational diseases in Guangdong Province from 2003 to 2019. The results showed that the measured value of new cases in all those years, except 2011, was within 95% confidence interval of the fitting value. The median(25 th, 75 th percentile) of absolute relative deviation between the fitting value and the actual value was 8.9%(4.3%, 14.7%). CONCLUSION: The regression model based on cubic curve can better fit the incidence of occupational diseases and can be used to describe the occurence of occupational diseases.

The Burden, Future Trends, And Economic Impact Of Lung Cancer In Saudi Arabia.

BACKGROUND: Incidence of cancer in Saudi Arabia has increased for the last two decades, ratcheting up to global levels. Yet, there is a dearth of research on the burden of lung cancer. This study examined the association between new cases of lung cancer and factors such as gender, age, and year of diagnosis; and forecast new cases and extrapolated future economic burden to 2030. METHODS: This a national-level cohort study that utilized the Saudi Cancer Registry data from 1999 to 2013. Multivariate regression was used; new lung cancer cases forecast and economic burden extrapolated to 20130. Sensitivity analysis was conducted to assess the impact of a range of epidemiologic and economic factors on the economic burden. RESULTS: Of the 166,497 new cancer cases (1999-2013), 3.8% was lung cancer. Males and Saudis had over threefold higher cases compared with females and non-Saudis, respectively. While the age group ≥65 years had 1.14 times or 14% increase in new cases, under-30 years had 97.2% fewer cases compared with age group 45-59. Compared with 1999, the period 2011-2013 had a 106% average increase. The years 2002-2010 registered an average 50% rise in new cases compared to 1999. New cases would rise to 1058 in 2030, an upsurge of 87% from 2013. The future economic burden was estimated at $2.49 billion in 2015 value, of which $520 million was attributable to care management and $1.97 billion in lost productivity. The economic burden for the period 2015-2030 will be $50.16 billion. The present value of this burden in 2015 values will be $34.60 billion, of which 21% will be attributable to care management. Estimates were robust to uncertainty, but the aged-standardized rate and 5-year survival rate would account for much of the variability compared with the economic factors. CONCLUSION: Findings reveal an upsurge of lung cancer burden in incidence and potential economic burden, which may inform cancer control measures.

Relationship Between Anaphylaxis and Use of Beta-Blockers and Angiotensin-Converting Enzyme Inhibitors: A Systematic Review and Meta-Analysis of Observational Studies.

BACKGROUND: Beta-blockers (BBs) and angiotensin-converting enzyme inhibitors (ACEIs) have been associated with an increased risk and severity of anaphylaxis. However, the evidence supporting these findings is contradictory. OBJECTIVE: We carried out a systematic review and meta-analysis of studies that assess the influence of BBs and ACEIs on anaphylaxis. METHODS: We searched PubMed/MEDLINE, EMBASE, the Cochrane Database of Systematic Reviews, and the Web of Science for relevant observational studies. We searched for studies where the presence and severity of anaphylaxis were compared between patients taking BBs, ACEIs, both types of drug, or neither type of drug. We performed a meta-analysis using a random-effects model. RESULTS: A total of 21 studies met the study criteria. Of these, 15 assessed the severity and 9 the incidence of anaphylaxis. The studies brought together 22,313 anaphylaxis episodes for the severity studies and 18,101 anaphylaxis episodes for the studies of new cases of anaphylaxis. BBs and ACEIs increased the severity of anaphylaxis (BBs, odds ratio [OR] 2.19, 95% confidence interval [CI] 1.25-3.84; ACEIs, OR 1.56, 95% CI 1.12-2.16), but not the presence of new cases of anaphylaxis (BBs, OR 1.40, 95% CI 0.91-2.14; ACEIs, OR 1.38, 95% CI 0.39-4.86). It was not possible to perform an analysis adjusted for cardiovascular diseases, because only 1 study each for BBs and ACEIs, respectively, had adjusted data. CONCLUSIONS: The quality of evidence showing that the use of BBs and ACEI increases the severity of anaphylaxis is low owing to differences in the control of confounders arising from the concomitant presence of cardiovascular diseases.

Tuberculosis in Poland in 2016

AIM OF THE STUDY: To evaluate the main features of TB epidemiology in 2016 in Poland and to compare with the situation in the EU/EEA countries. METHODS: Analysis of case- based data on TB patients from National TB Register, data on anti-TB drug susceptibility testing results in cases notified in 2016, data from National Institute of Public Health- National Institute of Hygiene on cases of tuberculosis as AIDS-defining disease, data from Central Statistical Office on deaths from tuberculosis based on death certificates, data from ECDC report " European Centre for Disease Prevention and Control/WHO Regional Office for Europe. Tuberculosis surveillance and monitoring in Europe 2018- 2016 data. Stockholm: European Centre for Disease Prevention and Control, 2018". RESULTS: In 2016, 6 444 TB cases were reported in Poland. The incidence rate was 16.8 cases per 100 000, with large variability between voivodships from 8.1 to 24.3 per 100 000. The average decline of TB incidence was 3.0% per year during 2012- 2016. 5 713 cases were new, never treated i.e. 14.9 per 100 000. 731 cases i.e. 1.9 per 100 000 ­ 11.3% of all registered subjects were previously treated. In 2016, 6 116 pulmonary tuberculosis cases occurred in Poland, equivalent to 15.9 per 100 000. Pulmonary cases represented 94.9% of all TB cases. The number of pulmonary TB cases with bacteriological confirmation was 4475 i.e. 12,0 per 100 000. In 2016, only 328 extrapulmonary TB cases were reported. Pediatric cases represented 1.6% of the total TB cases in Poland; 103 children with TB were notified. The incidence of tuberculosis has been growing along with the age group from 1.8 per 100 000 among children to 27.8 per 100 000 among patients aged 45 to 64 years. In the age group 65 years old and older the incidence was 26.0 per 100 000. The incidence among men i.e. 24.0 per 100 000 was >2 times higher than among women i.e. 10.0 per 100 000. The biggest difference in the TB incidence between the two sex groups occurred in persons aged 55 to 59 years ­ 51.9 vs. 11.5 and in subjects aged 60 to 64 years (45.9 vs. 11.7). The TB incidence in urban population was higher than in rural, respectively 17.3 per 100 000 and 15.9 per 100 000. In 2016, the number of all culture positive TB cases was 4619. Culture-confirmed cases represented 71.7% of all TB cases; culture-confirmed pulmonary TB ­ 73.2% of all pulmonary TB cases. In 2016, the number of smear-positive/culture positive pulmonary TB cases was 2612 (6.8 per 100 000) what represented 42.7% of all pulmonary TB cases. TB was initial AIDS indicative disease in 17 persons. In 2016, 46 cases with MDR-TB (among them 10 foreigners) and 101 patients with resistance solely to isoniazid were reported in Poland, representing respectively 1.1% and 2.4% of cases with known DST results (DSTs were done in 90.7% of all culture-confirmed TB cases). In 2016, there were 92 TB cases of foreign origin. In 2015, there were 537 deaths due to tuberculosis in Poland, which is equivalent to 1.4 deaths per 100 000 population; 520 people died from pulmonary and 17 from extrapulmonary tuberculosis. Mortality among males ­ 2.3 per 100 000 ­ was 3.8 x higher than among females ­ 0.6. The highest mortality rate was observed in subjects 65 years old and older ­ 3.3 per 100 000. There were no deaths from tuberculosis in children and adolescents. In 2015, TB mortality represented 0.14% of total mortality in Poland and 28.0% of mortality from infectious diseases. CONCLUSIONS: In 2016, the incidence of tuberculosis in Poland was slightly higher than in 2015 and higher than the average in the EU/EEA countries. The highest incidence rates were observed in older age groups. The incidence in males was more than 2 times higher than in females. The impact of migration on the characteristics of tuberculosis in Poland is low. In Poland, tuberculosis in children, tuberculosis in persons infected with HIV and MDR-TB is less common than the average in the EU/EEA countries.