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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Impact of Regulatory Interventions on Ulipristal Acetate 5 mg (Esmya) Use in Spain: An Interrupted Time-Series Analysis.

PURPOSE: Since late 2017, the use of ulipristal acetate 5 mg (UPA; Proprietary name: Esmya) has been under review in the European Union, due to an emerging hepatic risk. In February 2018 and in July 2018, the Spanish Agency of Medicines and Medical Devices and the marketing authorization holder put two risk minimization measures (RMM) in place, in order to inform about new safety information and to mitigate this risk. This study aims to assess RMM effectiveness in Spain, by performing an interrupted time-series (ITS) analyses, between 2014 and 2019. METHOD: Two quasi-experimental ITS analyses to examine the use of UPA before and after the RMM release were performed: (a) an ecological study using aggregated data from a drug consumption database; and (b) a study using primary healthcare data gathered from electronic clinical records. RESULTS: Regulatory interventions were associated with an immediate and significant decrease level of DID (the number of DDD dispensed per 100 000 inhabitants and day) and incidence. The DID was 70% less than expected 12 months after the interventions. This value was 59% for the incidence. However, a change in the slope was not observed and the use started rising again in the last segment of the study period. CONCLUSION: Despite RMM had an immediate strong impact on UPA use, the last segment upward trend in the long-term might have been affected by the lack of comparable therapeutic alternatives. Further studies should be performed to confirm the increase trend observed and analyze subsequent measures and additional data.

How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model.

Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium (2H) and oxygen-18 (18O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ18O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ18O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

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.

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.

Spatial and temporal analysis of breast cancer mortality in a state in northeastern Brazil.

Breast cancer (BC) is the most common neoplasm, and its global burden has become one of the most important factors jeopardizing the health of the world population, especially women. The aim of this study was to analyze mortality trends and the spatial distribution of BC in women in the capital and state of Sergipe, aiming to contribute to the implementation and improvement of strategies for the prevention and health promotion of women with BC. Trends were calculated using the Joinpoint Regression Program 5.0.2. Spatial analyses were performed using the empirical Bayesian model, thematic maps were created using QGIS 3.10.7 and Moran's I indices were calculated using TerraView 4.2.2. Between 1996 and 2022, 1384 and 3128 BC deaths were recorded in the capital and state of Sergipe, respectively. The mortality trend increased in the age groups of 45-75+ for the state of Sergipe, while in the capital, we observed stability in all age groups. The highest AAPC was 4.6213, with a 95 % confidence interval (2.16; 7.14). Univariate global Moran's I analysis indicated spatial autocorrelation during the study period. A direct relationship was found between mortality rates and the more economically developed regions.

Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.

During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

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.

ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern.

There is considerable evidence that action potentials are accompanied by "intrinsic optical signals", such as a nanometer-scale motion of the cell membrane. Here we present ChiSCAT, a technically simple imaging scheme that detects such signals with interferometric sensitivity. ChiSCAT combines illumination by a chaotic speckle pattern and interferometric scattering microscopy (iSCAT) to sensitively detect motion in any direction. The technique features reflective high-NA illumination, common-path suppression of vibrations, and a large field of view. This approach maximizes sensitivity to motion, but does not produce a visually interpretable image. We show that unsupervised learning based on matched filtering and motif discovery can recover underlying motion patterns and detect action potentials. We demonstrate these claims in an experiment on blebbistatin-paralyzed cardiomyocytes. ChiSCAT opens the door to action potential measurement in scattering tissue, including a living brain.