Characterization of the reproductive strategy of invasive Round Goby (Neogobius melanostomus) in the Upper Danube River.

Originating from the Black and Caspian seas, the Round Goby (Neogobius melanostomus) has become one of the most successful invaders of freshwater ecosystems. In this study, we provide a characterization of the reproductive strategy of an established population of Round Gobies in the Upper Danube river including sex ratio, fluctuations of gonadosomatic index (GSI), analysis of timing of spawning as well as of clutch and egg size. We compare these results to other studies from the native and invaded range. In the Danube, the Round Goby population was found to be female dominated, however fluctuations in magnitude of female bias were observed between months. Monitoring of the population across 1.5 years revealed that GSI was highest from April to June, while lowest values were observed in August and September. Using time-series analysis, a delayed effect of temperature on GSI was found for females and males, while a quicker response of GSI levels to photoperiod and discharge was observed for females. GSI increased with body size for females and eggs were found to be significantly larger in May, however clutch sizes did not differ between months. Results of a literature review revealed great differences in timing and length of spawning season as well as sex ratio between populations throughout the distribution range, which can probably be explained by climatic and photoperiodic conditions together with the time since invasion and the high plasticity of Round Gobies.

Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study.

Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P < 0.001). A 1% increment and decrement in average relative humidity contributed to an increase of 115 and 153 TB cases, respectively. A 1 hPa increment and decrement in average air pressure contributed to a decrease of 318 and 91 TB cases, respectively (WLR = 7.966, P = 0.005). ∆temperature(-), ∆(sunshine hours)( -), ∆(wind velocity)(+) and ∆(wind velocity)(-) at different lags had a meaningful short-run effect on TB. The NARDL outperformed the ARDL in forecasting. Climate variation has significant long- and short-run asymmetric impacts on TB. By incorporating both dimensions of effects into the NARDL, the accuracy of the forecasts and policy recommendations for TB can be enhanced.

Dynamics of following and leading: association of movement synchrony and depression severity.

Objective: Depression negatively affects interpersonal functioning and influences nonverbal behavior. Interpersonal theories of depression suggest that depressed individuals engage in behaviors that initially provoke others' support and reassurance, but eventually lead to rejection that may also be expressed nonverbally. Methods: This study investigated movement synchrony as a nonverbal indicator of support and rejection and its association with depression severity in a sample of depressed and healthy individuals. Semi-standardized diagnostic interview segments with N = 114 dyads were video recorded. Body movement was analyzed using Motion Energy Analysis, synchrony intervals were identified by computing windowed cross-lagged correlation and a peak-picking-algorithm. Depression severity was assessed via both self-rating (BDI-II) and clinician rating (HAMD). Results: Both self-rated and clinician-rated depression severity were negatively correlated with patient-led, but not clinician-led movement synchrony measures. The more depressed patients were, the less they initiated movement synchrony with their clinicians. These correlations remained significant after controlling for gender, age, gross body movement, and psychopharmacological medication. Conclusion: Findings suggest that depression may negatively affect patients' active initiative in interaction situations. Automatized methods as used in this study can add valuable information in the diagnosis of depression and the assessment of associated social impairments.

Heatwave and upper urinary tract stones morbidity: effect modification by heatwave definitions, disease subtypes, and vulnerable populations.

As heatwave occurs with increased frequency and intensity, the disease burden for urolithiasis, a heat-specific disease, will increase. However, heatwave effect on urolithiasis subtypes morbidity and optimal heatwave definition for urolithiasis remain unclear. Distributed lagged linear models were used to assess the associations between 32 defined heatwave and upper urinary tract stones morbidity. Relative risk (RR) and attributable fraction (AF) of upper urinary tract stone morbidity associated with heatwave of different intensities (low, middle, and high) were pooled by meta-analysis. Optimal heatwave definition was selected based on the combined score of AF, RR, and quasi-Akaike Information Criterion (QAIC) value. Stratified analyses were conducted to investigate the modification effects of gender, age, and disease subtypes. Association between heatwave and upper urinary tract stones morbidity was mainly for ureteral calculus, and AF was highest for low-intensity heatwave. This study's optimal heatwave was defined as average temperature > 93rd percentile for ≥ 2 consecutive days, with AF of 7.40% (95% CI: 2.02%, 11.27%). Heatwave was associated with ureteral calculus morbidity in males and middle-aged adults. While heatwave effect was statistically insignificant in females and other age groups. Managers should develop appropriate definitions to address heatwave based on regional characteristics and focus on heatwave effects on urolithiasis.

Changes in Mental Health Among Adolescents in South Korea Before and After COVID-19: An Interrupted Time Series Analysis From 2015 to 2022.

PURPOSE: This study aimed to examine changes in mental health among adolescents by comparing data from the period following the onset of the COVID-19 pandemic with the period before the pandemic. METHODS: We estimated the annual prevalence of stress perception, depressive symptoms, and suicidal ideation among middle and high school students using data from the Korean Youth Health Behavior Survey spanning from 2015 to 2022. We then compared mental health status across 2 periods-pre-COVID-19 (2015-2019) and during COVID-19 (2020-2022)-employing an interrupted time series analysis. We adjusted for covariates, such as household economic status, residence type, self-rated health, and history of hospitalization, due to violence. RESULTS: We analyzed data from 472,385 adolescents (242,819 boys and 230,016 girls). Stress perception, depressive symptoms, and suicidal ideation showed an increasing trend during the pre-COVID-19 period, followed by a decrease in the first year of the pandemic and an increasing trend in the second and third years. Boys experienced a faster increase in stress and depressive symptoms during the second and third years of the pandemic compared with the pre-COVID-19 period, whereas girls showed trends similar to those observed before the pandemic. Middle school students experienced a more rapid increase in these indicators than high school students during the second and third years. DISCUSSION: Adolescents' mental health initially improved in the first year of COVID-19 but worsened during the second and third years of the pandemic. This suggests a need for intervention policies and programs to support adolescent mental health.

Predicting herd immunity achievement: a time-series analysis of vaccination and fatality rates using 1,075 days of COVID-19 data.

Introduction: The COVID-19 pandemic, driven by SARS-CoV-2, has made vaccination a critical strategy for global control. However, vaccine hesitancy, particularly among certain age groups, remains a significant barrier to achieving herd immunity. Methods: This study uses Poisson regression and ARIMA time-series modeling to identify factors contributing to vaccine hesitancy, understand age-specific vaccination preferences, and assess the impact of bivalent vaccines on reducing hesitancy and fatality rates. It also predicts the time required to achieve herd immunity by analyzing factors such as vaccine dosing intervals, age-specific preferences, and changes in fatality rates. Results: The study finds that individuals recovering from COVID-19 often delay vaccination due to perceived immunity. There is a preference for combining BNT162b2 and CoronaVac vaccines. The BNT162b2 bivalent vaccine has significantly reduced vaccine hesitancy and is linked with lower fatality rates, particularly in those aged 80 and above. However, it tends to induce more severe side effects compared to Sinovac. Vaccine hesitancy is most prevalent among the youngest (0-11) and oldest (80+) age groups, posing a challenge to reaching 90% vaccination coverage. Conclusion: Vaccine hesitancy is a major obstacle to herd immunity. Effective strategies include creating urgency, offering incentives, and prioritizing vulnerable age groups. Despite these challenges, the government should have continued to encourage vaccinations while gradually lifting COVID-19 control measures, balancing public health safety with the return to normal life, as was observed in the transition period during the latter stages of the pandemic.

Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.

PURPOSE: This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD). METHODS: A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset. RESULTS: EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors. CONCLUSIONS: Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study.

BACKGROUND: Epidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent. OBJECTIVES: We used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period. METHODS: Ninety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors. RESULTS: After adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0-1 day (0.31, 95 % confidence interval [CI]: 0.24-0.38), 0-7 days (0.68, 95 % CI: 0.57-0.79), and 0-50 days (1.02, 95 % CI: 0.82-1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0-1 day (0.36, 95 % CI: 0.25-0.47), 0-7 days (0.62, 95 % CI: 0.46-0.77), and 0-50 days (1.27, 95 % CI: 0.87-1.67) lag periods. Over a 0-7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (ß = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (ß = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (ß = 0.85 vs. 0.45; P_int = 0.028), diabetics (ß = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (ß = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0-50 day lag, males (ß = 0.99 vs. 0.96; P_int < 0.0001), diabetics (ß = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (ß = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (ß = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses. CONCLUSIONS: Prolonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

The relationship of short-term exposure to meteorological factors on diabetes mellitus mortality risk in Hefei, China: a time series analysis.

OBJECTIVES: The study aims to explore whether short-term exposure to meteorological factors has a potential association with the risk of diabetes mellitus (DM) mortality. METHODS: During the period 2015-2018, we collected daily data on meteorological factors and deaths of diabetic patients in Hefei. A total of 1101 diabetic deaths were recorded. We used structural equation modeling to initially explore the relationships among air pollutants, meteorological variables, and mortality, and generalized additive modeling (GAM) and distributional lag nonlinear modeling (DLNM) to explore the relationship between meteorological factors and the mortality risk of DM patients. We also stratified by age and gender. The mortality risk in diabetic patients was expressed by relative risks (RR) and 95% confidence intervals (CI) for both single and cumulative days. RESULTS: Single-day lagged results showed a high relative humidity (RH) (75th percentile, 83.71%), a fairly high average temperature (T mean) (95th percentile, 30.32 °C), and an extremely low diurnal temperature range (DTR) (5th percentile, 3.13 °C) were positively related to the mortality risk of DM. Stratified results showed that high and very high levels of T mean were significantly positively linked to the mortality risk of DM among females and the elderly, while very high levels of DTR were linked to the mortality risk in men and younger populations. CONCLUSION: In conclusion, this study found that short-duration exposure to quite high T mean, high RH, and very low DTR were significantly positively related to the mortality risk of DM patients. For women and older individuals, exposure to high and very high T mean environments should be minimized. Men and young adults should be aware of daily temperature changes.

THE SILENT CRISIS: RISING TRENDS IN ADOLESCENT SUICIDE IN BRAZIL

PurposeThis study aims to analyze trends in youth suicide rates and the odds of suicide comparing different age groups from 2000 to 2022 in Brazil. MethodsWe conducted a temporal trend study on suicide mortality in Brazil from 2000 to 2022, using data from the Mortality Information System and population projections from the Brazilian Institute of Geography and Statistics (IBGE). We calculates mortality rates for the following age groups: adolescents (10-19 years), young adults (20-29 years), youth (10-29 years), adults including youth (20 years and older), and adults excluding youth (30 years and older). We applied Joinpoint regression to identify changes in trends and calculated the Mortality Odds Ratio (MOR) to compare suicide mortality between youth and adults. ResultsBetween 2000 and 2022, suicide mortality significantly increased among adolescents, with a higher average annual percentage change compared to young adults. The Mortality Odds Ratio (MOR) for youth was 5.05 in 2000, rising to 8.56 in 2022. For adolescents, the MOR increased from 3.27 to 8.86 over the same period, indicating that in 2022, the suicide risk was 21% higher among adolescents than young adults, highlighting a significant reversal in the context of the Covid-19 pandemic. DiscussionThese findings underscore the urgent need for national mental health policies targeted at adolescents, particularly those in high vulnerability. The results of this study provide an evidence base for policymakers and public health officials to develop and implement interventions aimed at curbing the rising trend of youth suicide in Brazil.

Impact of Public Health and Social Measures on Cosmetic Treatments in the COVID-19 Pandemic: A Retrospective Multi-Center Study Combined With a Questionnaire-Based Cross-Sectional Study.

BACKGROUND: Public health and social measures (PHSMs) are considered the most effective approaches for controlling epidemic diseases. This study aimed to explore variations in the time-dependent characteristics of and public preferences for cosmetic treatments during and after the implementation of PHSMs during the COVID-19 pandemic. METHOD: Medical records from six medical institutions were extracted retrospectively. Time-series analyses were conducted to reveal the variations in characteristics in volume and proportion of cosmetic treatments according to PHSMs. A cross-sectional study was conducted with online questionnaire designed for the general population during and after the implementation of PHSMs. RESULT: A total of 141 033 records were included in this retrospective study. The implementation of PHSMs led to extremely low treatment volumes; compared with the increases in private hospitals, the treatment volumes in public hospitals exhibited earlier and more significant increases, even higher than pre-PHSM levels (p < 0.05), which mainly contributed to the increase in plastic surgery volumes during and after the implementation of PHSMs. The differences in the anxiety state, self-perceived appearance, and cosmetic treatment intentions of the participants were illustrated during and after PHSMs. We further demonstrated the participants' decisions on cosmetic treatments after the implementation of PHSMs during the COVID-19 pandemic. CONCLUSION: The immediate effects and aftereffects of PHSMs on cosmetic treatments were different in public and private hospitals. Furthermore, as PHSMs guided the adjustment of cosmetic treatments in the post-COVID-19 era, the intention to undergo cosmetic treatment during PHSMs was associated with the anxiety states and preferences of the population.

Effects of transparent online open procurement on prices, volumes, and costs of medicines: an interrupted time series study in Ningxia, China.

Objectives: To assess the effects of the transparent online open procurement arrangement on the prices, volumes, and costs of medicines in Ningxia, China. Methods: Data were extracted from the Ningxia pharmaceutical procurement platform, covering 16 months of purchase orders (December 2019 to March 2021) prior to the implementation of the transparent online open procurement policy and 20 months of purchase orders after the implementation of the policy (April 2021 to November 2022). Interrupted time series (ITS) analysis was performed to evaluate the effects of the transparent online open procurement policy on the prices, volumes, and total costs of the purchase orders. Results: After implementation of the transparent online open procurement policy, the average price of purchased medicines showed a declining trend by 0.012 Yuan per month, while the total volume of purchase orders declined at a rate by 1.741 million per month measured by the smallest formulation units and the total costs of the purchase orders decreased at a rate by 5.525 million Yuan per month. Conclusion: The transparent online open procurement policy resulted in reduced prices, lowered volumes, and lowered total costs of purchased orders of medicines.

Assessing the impact of COVID-19 non-pharmaceutical interventions and relaxation policies on Class B respiratory infectious diseases transmission in China.

This study investigates the incidence of Class B respiratory infectious diseases (RIDs) in China under the Coronavirus disease 2019 (COVID-19) epidemic and examines variations post-epidemic, following the relaxation of non-pharmaceutical interventions (NPIs). Two-stage evaluation was used in our study. In the first stage evaluation, we established counterfactual models for the pre-COVID-19 period to estimate expected incidences of Class B RIDs without the onset of the epidemic. In the second stage evaluation, we constructed seasonal autoregressive integrated moving average intervention (SARIMA-Intervention) models to evaluate the impact on the Class B RIDs after NPIs aimed at COVID-19 pandemic were relaxed. The counterfactual model in the first stage evaluation suggested average annual increases of 10.015%, 78.019%, 70.439%, and 67.799% for tuberculosis, scarlet fever, measles, and pertussis respectively, had the epidemic not occurred. In the second stage evaluation, the total relative reduction in 2023 of tuberculosis, scarlet fever, measles and pertussis were - 35.209%, - 59.184%, - 4.481%, and - 9.943% respectively. The actual incidence declined significantly in the first stage evaluation. However, the results of the second stage evaluation indicated that a rebound occurred in four Class B RIDs after the relaxation of NPIs; all of these showed a negative total relative reduction rate.

An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM.

Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.

Epidemiological characterization of hand, foot, and mouth disease among hospitalized children from 2014 to 2023 in a hospital in Henan Province: Longitudinal surveillance study.

Hand, foot, and mouth disease (HFMD) is an acute infectious illness primarily caused by enteroviruses. The present study aimed to describe the epidemiological characteristics of hospitalized HFMD patients in a hospital in Henan Province (Zhengzhou, China), and to predict the future epidemiological parameters. In this study, we conducted a retrospective analysis of general demographic and clinical data on hospitalized children who were diagnosed with HFMD from 2014 to 2023. We used wavelet analysis to determine the periodicity of the disease. We also conducted an analysis of the impact of the COVID-19 epidemic on the detection ratio of severe illness. Additionally, we employed a Seasonal Difference Autoregressive Moving Average (SARIMA) model to forecast characteristics of future newly hospitalized HFMD children. A total of 19 487 HFMD cases were included in the dataset. Among these cases, 1515 (7.8%) were classified as severe. The peak incidence of HFMD typically fell between May and July, exhibiting pronounced seasonality. The emergence of COVID-19 pandemic changed the ratio of severe illness. In addition, the best-fitted seasonal ARIMA model was identified as (2,0,2)(1,0,1)12. The incidence of severe cases decreased significantly following the introduction of the vaccine to the market (χ2 = 109.9, p < 0.05). The number of hospitalized HFMD cases in Henan Province exhibited a seasonal and declining trend from 2014 to 2023. Non-pharmacological interventions implemented during the COVID-19 pandemic have led to a reduction in the incidence of severe illness.

COVID-19 impacted the suicidal ideation among health care workers and medical students: An interrupted time-series analysis of data from 30 countries.

AIM: To investigate the impact of public health emergencies on the prevalence of suicidal ideation among healthcare workers (HCWs) and medical students. METHODS: The prevalence of suicidal ideation among HCWs and medical students was searched for analysis. The platforms included PubMed, medRVix, bioRvix, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science. Interrupted time-series analysis was employed to determine whether the COVID-19 pandemic influenced the prevalence and trends of suicidal ideation. To account for autocorrelation and heteroskedasticity, Newey-West standard errors were utilized with a lag of order one. RESULTS: Seventy studies with 145,641 HCWs and medical students from 30 countries were included in the final analysis, with 30 studies before COVID-19 and 40 studies during the pandemic. Before the pandemic outbreak (April 2020), the monthly increasing rate was 0.063 % (95 % CI: -0.009 %, 0.135 %, z = 1.73, P = 0.084). The tendency of suicidal ideation prevalence increased by 1.116 % (95%CI: 0.888 %, 1.344 %, z = 9.60, P < 0.001). In other words, the calculated monthly growth rate of suicidal ideation after the pandemic outbreak is 1.179 % (95%CI: 0.968 %, 1.391 %, z = 10.93, P < 0.001) per month. The overall growing trend of prevalence of suicidal ideation during the pandemic is 1.896 % per month in America; 1.590 % in Europe; 0.443 % (95%CI: 0.213 %, 0.673 %, z = 3.77, P < 0.001) in Asia; 1.055 % in HCWs; and 0.645 % in medical students. CONCLUSION: This study highlights that the COVID-19 pandemic can significantly impact the prevalence of suicidal ideation among HCWs and medical students, and the prevalence showed an upward trend.

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis.

BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks. METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions. RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk. CONCLUSION: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

The association between introduction of the micro-axial flow pump Impella in hospitals and in-hospital mortality in patients treated with extracorporeal membrane oxygenation: interrupted time-series analyses.

BACKGROUND: The micro-axial flow pump Impella, a new mechanical circulatory device for cardiogenic shock, is still only available in a limited number of hospitals, due to the facility certification requirements and insufficient evidence of the benefit of introducing Impella in hospitals. This study aimed to evaluate the impact of introducing Impella in hospitals on in-hospital mortality of patients treated with extracorporeal membrane oxygenation (ECMO). METHODS: Using a nationwide Japanese inpatient database, we identified patients who received ECMO during hospitalization between 1 April 2014 and 31 March 2021. A hospital-level propensity score-matched cohort was created matching hospitals that introduced Impella (exposure group) to those that did not introduce Impella (control group). The inclusion period in each hospital was divided into two time periods according to the time of Impella introduction in the exposure group and the corresponding hospital in the control group (before and after exposure). The primary outcome was in-hospital mortality. Uncontrolled and controlled interrupted time-series analyses involved before-after exposure comparison and exposure-control comparison. RESULTS: Out of 34,379 eligible patients, we created a matched cohort of 8351 patients from 86 hospitals with Impella introduction (exposure group) and 7230 patients from 86 hospitals without Impella introduction (control group). In-hospital mortality before and after exposure was 62.5% and 59.3, respectively, in the exposure group; and 66.8% and 63.7%, respectively, in the control group. Uncontrolled interrupted time-series analysis showed no significant level change or trend change in the before-after exposure comparison in both the exposure and the control groups. Controlled interrupted time-series analysis also showed no significant level change (-0.01%; 95% confidence intervals -5.36% to + 5.33%) or trend change (+ 0.10%, -0.30% to + 0.40%) after exposure in the exposure-control comparison. CONCLUSIONS: This nationwide inpatient database study showed no association between Impella introduction in hospitals and in-hospital mortality of patients who underwent ECMO. Because this study confined itself to analze of the impact of the introduction of Impella solely at the hospital level, further detailed studies are warranted to assess its efficacy at the patient level.

Structural Damage Detection under Ambient Excitation Using Symbolic Three-Order Square Matrix Formed by Specific-Interval-Sampled Time-Domain Signals.

In the structural health monitoring of vibration systems, varying excitation always affects the accuracy of damage identification. The proposed symbolic three-order square matrix damage detection method with the matrix norm as a damage indicator can solve the difficult problem of damage identification under ambient excitation. The new sampling pattern extracts data from signals in the time domain at specific intervals based on the structural properties with the help of the autocorrelation coefficient. Then, the data extracted are converted into symbols and arranged into a three-order square matrix, and the Frobenius norm of the matrix is used for structural damage identification as a reliable damage indicator. In this process, the transmissibility function is employed to eliminate the effects of varying excitation. First, the method was verified by a cracked simply supported beam-a simulated Abaqus model. Then, a wooden truss bridge in the laboratory and an actual engineering scenario under ambient excitation together demonstrated the effectiveness and accuracy of the damage identification method and proved the proposed method to be robust to different types of damage under ambient excitation. Compared with other related methods, this method is more intuitive and efficient.

The reporting quality of meta-epidemiological studies needs substantial improvement: a research on research study.

BACKGROUND: Meta-epidemiological research plays a vital role in providing empirical evidence needed to develop methodological manuals and tools, but the reporting quality has not been comprehensively assessed, and the influence of reporting guidelines remains unclear. The current study aims to evaluate the reporting quality of meta-epidemiological studies, assess the impact of reporting guidelines, and identify factors influencing reporting quality. METHODS: We searched PubMed and Embase for meta-epidemiological studies. The reporting quality of these studies was assessed for adherence to established reporting guidelines. Two researchers independently screened the studies and assessed the quality of the included studies. Time-series segmented linear regression was used to evaluate changes in reporting quality over time, while beta-regression analysis was performed to identify factors significantly associated with reporting quality. RESULTS: We initially identified 1720 articles, of which 125 meta-epidemiological studies met the inclusion criteria. Of these, 65 (52%) had low reporting quality, 60 (48%) had moderate quality, and none achieved high quality. Of the 24 items derived from established reporting guidelines, 4 had poor adherence, 13 had moderate adherence, and 7 had high adherences. High journal impact factor (≥ 10) (OR = 1.42, 95% CI: 1.13, 1.80; P = 0.003) and protocol registration (OR = 1.70, 95% CI: 1.30, 2.22; P < 0.001) were significantly associated with better reporting quality. The publication of the reporting guideline did not significantly increase the mean reporting quality score (- 0.53, 95% CI: - 3.37, 2.31; P = 0.67) or the trend (- 0.38, 95% CI: - 1.02, 0.26; P = 0.20). CONCLUSIONS: Our analysis showed suboptimal reporting quality in meta-epidemiological studies, with no improvement post-2017 guidelines. This potential shortcoming could hinder stakeholders' ability to draw reliable conclusions from these studies. While preregistration could reduce reporting bias, its adoption remains low. Registration platforms could consider creating tailored types for meta-epidemiological research, and journals need to adopt more proactive measures to enforce reporting standards.