Change in Contraceptive Use Within South Carolina Medicaid Following the Choose Well Contraceptive Access Initiative: Did COVID-19 Alter the Trends?

Objectives: COVID-19 hit at the midpoint of Choose Well, a statewide contraceptive access initiative commenced in South Carolina (SC) in 2017. This study assessed whether the pandemic altered the trends in contraceptive use among SC Medicaid during the first half of Choose Well. Methods: Contraception use among 333,253 women was analyzed from 2017 to 2022, divided into prepandemic (January 2017-February 2020) and pandemic (March 2020-December 2022) periods. Bivariate differences in contraceptive use were examined using Pearson's Chi square test across these periods, including the first, first two, and first three quarters of the pandemic. Interrupted time-series analysis assessed changes in trends for intrauterine devices (IUDs) and implants during pandemic compared with prepandemic levels. Results: IUD and implant use dropped during the first two quarters of the pandemic. While IUD use matched the prepandemic levels by the end of the first three quarters, implant use slightly lagged. The use of injections and pills decreased from 16.6% and 26.2% during the prepandemic period to 13.6% and 21.7% during the pandemic period, respectively (p < 0.001). The trends in IUD and implant use in the pandemic period were higher by 0.01 (95% confidence interval [CI]: 0.01, 0.02) and 0.04 (95% CI: 0.03, 0.05) percentage points per month relative to the prepandemic trends, respectively. Conclusions: The pandemic's initial impact quickly stabilized, and overall, the gains in contraceptive use among Medicaid beneficiaries associated with Choose Well remained largely unaffected, with some methods showing increased trends.

Early effect of a financial incentive for surgeries within 48 h after hip fracture on the number of expedited hip fracture surgeries, in-hospital mortality, perioperative morbidity, length of stay and inpatient medical expenses.

OBJECTIVE: To examine the early effects of the financial incentive (FI) implemented in April 2022 in Japan for surgeries within 48 h after hip fracture (HF) in patients aged 75 and older on expedited HF surgery (EHFS), in-hospital mortality, perioperative morbidity, length of stay (LOS) and inpatient medical expenses (IMEs). STUDY SETTING AND DESIGN: We conducted a quasi-experimental study and constructed segmented regression models for controlled interrupted time-series analyses, assuming a Poisson distribution, to evaluate the slope changes (SCs) in the outcomes of interest before and after the introduction of the FI. DATA SOURCES AND ANALYTIC SAMPLE: We used Diagnosis Procedure Combination data from the Quality Indicator/Improvement Project database between 1 April 2018 and 31 March 2023. Patients aged 50 years or older who were hospitalized with a diagnosis of HF and underwent surgery for HF were included. PRINCIPAL FINDINGS: A total of 82,163 patients from 183 hospitals were included in the analyses. In the age group of 75 years and older, increasing trends in the number of EHFSs were observed even before the introduction of the FI, while before and after the introduction of the FI, none of the SCs in the monthly number of EHFSs within 2 days, within 1 day, and on the day of admission were statistically significant (incident rate ratio: 1.0043, 95% confidence interval [CI]: [0.9977-1.0111], 1.0068 [0.9987-1.0149], 1.0073 [0.9930-1.0219]). Nor were any of the SCs in in-hospital deaths, perioperative complications, LOS, and IMEs statistically significant. Additionally, there were no statistical differences in the SCs for any of the outcomes between the two age groups. CONCLUSION: This study suggested that there was no significant, short-term effect of the FI for surgeries within 48 h after HF on any of the outcomes of interest.

Association Between School-Related Google Trends Search Volume and Suicides Among Children and Adolescents in Japan During 2016-2020: Retrospective Observational Study With a Time-Series Analysis.

BACKGROUND: Suicide is the leading cause of death among children and adolescents in Japan. Internet search volume may be useful in detecting suicide risk. However, few studies have shown an association between suicides attempted by children and adolescents and their internet search volume. OBJECTIVE: This study aimed to examine the relationship between the number of suicides and the volume of school-related internet searches to identify the search terms that could serve as the leading indicators of suicide prevention among children and adolescents. METHODS: We used data on weekly suicides attempted by elementary, middle, and high school students in Japan from 2016 to 2020, provided by the National Police Agency. Internet search volume was weekly data for 20 school-related terms obtained from Google Trends. Granger causality and cross-correlation analysis were performed to estimate the temporal back-and-forth and lag between suicide deaths and search volume for the related terms. RESULTS: The search queries "I do not want to go to school" and "study" showed Granger causality with suicide incidences. The cross-correlation analysis showed significant positive correlations in the range of -2 to 2 for "I do not want to go to school" (highest value at time lag 0, r=0.28), and -1 to 2 for "study" (highest value at time lag -1, r=0.18), indicating that the search volume increased as the number of suicides increased. Furthermore, during the COVID-19 pandemic period (January-December 2020), the search trend for "I do not want to go to school," unlike "study," was highly associated with suicide frequency. CONCLUSIONS: Monitoring the volume of internet searches for "I do not want to go to school" could be useful for the early detection of suicide risk among children and adolescents and for optimizing web-based helpline displays.

Suicide prevention-related Google searches and subsequent emergency department visits in California and Arizona, 2007-2015.

Introduction: United States emergency departments (ED) visit rates for nonfatal self-harm increased by 42% from 2001 to 2016. Previous suicide mortality research has provided conflicting evidence on the use of suicide-related Internet searches as a surveillance tool for self-harm and suicidal ideation. However, few have used rigorous approaches to account for autocorrelation at the aggregate level, and none have focused on Internet searches related to suicide prevention. Methods and results: Over a 9-year study period (2007-2015), suicidality-related search data were extracted using the Google Health Application Programming Interface (API) for Arizona and California - states, chosen for their differing age distributions and rigorous ED injury coding policies. We examined several combined suicide prevention-related search queries. Using autoregressive integration moving average (ARIMA) models and a Box-Jenkins approach, we assessed whether increased prevention-related Internet searches related to suicidality are predictive of lower subsequent ED visits related to suicidal ideation with or without self-harm injury. In both states, greater prevention-related queries were associated with lower ED visits approximately four to six weeks later. Conclusions: Our results indicate that Internet-based search volumes related to suicide prevention may have the potential to monitor suicidality and online suicide prevention resources offer meaningful opportunities for mental health support.

Deep learning based automatic detection and dipole estimation of epileptic discharges in MEG: a multi-center study.

Magnetoencephalography (MEG) provides crucial information in diagnosing focal epilepsy. However, dipole estimation, a commonly used analysis method for MEG, can be time-consuming since it necessitates neurophysiologists to manually identify epileptic spikes. To reduce this burden, we developed the automatic detection of spikes using deep learning in single center. In this study, we performed a multi-center study using six MEG centers to improve the performance of the automated detection of neuromagnetically recorded epileptic spikes, which we previously developed using deep learning. Data from four centers were used for training and evaluation (internal data), and the remaining two centers were used for evaluation only (external data). We used a five-fold subject-wise cross-validation technique to train and evaluate the models. A comparison showed that the multi-center model outperformed the single-center model in terms of performance. The multi-center model achieved an average ROC-AUC of 0.9929 and 0.9426 for the internal and external data, respectively. The median distance between the neurophysiologist-analyzed and automatically analyzed dipoles was 4.36 and 7.23 mm for the multi-center model for internal and external data, respectively, indicating accurate detection of epileptic spikes. By training data from multiple centers, automated analysis can improve spike detection and reduce the analysis workload for neurophysiologists. This study suggests that the multi-center model has the potential to detect spikes within 1 cm of a neurophysiologist's analysis. This multi-center model can significantly reduce the number of hours required by neurophysiologists to detect spikes.

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.

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.

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.

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.

The Effects of Removing the Requirement for Prior Reimbursement Approval on Anticoagulant Use in Ireland: A Cross-Sectional Study.

PURPOSE: Prior approval for reimbursement is a policy of cost containment while ensuring oversight and governance of medicines. It has been employed in Ireland to address financial challenges due to the shift from warfarin to direct oral anticoagulants (DOACs). Studies assessing the effectiveness of this policy are limited. Thus, we aimed to examine the effectiveness of prior approval for reimbursement of DOACs (apixaban, rivaroxaban) as a cost containment policy in Ireland. METHODS: The Irish Health Service Executive-Primary Care Reimbursement Service database was used in this cross-sectional study. We examined the prescribing frequencies and associated costs of the oral anticoagulants; [(OACs) apixaban, rivaroxaban and warfarin] listed in the top 100 most frequently prescribed drugs, between 2018 and 2021. Time series negative binomial regression was used to assess the impact of removing the approval requirement of apixaban in September 2019 followed by the other DOACs in November 2020. RESULTS: The prescribing frequency of OACs increased by almost 20% from 2018 to 2021. This study showed there were significant differences in the proportion of OACs prescribed among the Community Drug Schemes. A statistically significant decreased use of apixaban (< 1%, p < 0.05) occurred when prior approval was removed for all DOACs. CONCLUSIONS: The removal of prior approval for reimbursement of DOACs in Ireland had a minimal impact on the prescribing frequency trends of the OACs. Future use of these potentially useful policies by healthcare systems requires careful consideration of drug type, approval criteria and length of time the policy remains in place to minimise any negative effects associated with their use.

Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators.

The transcriptional regulatory network (TRN) is a graph framework that helps understand the complex transcriptional regulation mechanisms in the transcription process. Identifying the phenotype-specific transcription regulators is vital to reveal the functional roles of transcription elements in associating the specific phenotypes. Although many methods have been developed towards detecting the phenotype-specific transcription elements based on the static TRN in the past decade, most of them are not satisfactory for elucidating the phenotype-related functional roles of transcription regulators in multiple levels, as the dynamic characteristics of transcription regulators are usually ignored in static models. In this study, we introduce a novel framework called DTGN to identify the phenotype-specific transcription factors (TFs) and pathways by constructing dynamic TRNs. We first design a graph autoencoder model to integrate the phenotype-oriented time-series gene expression data and static TRN to learn the temporal representations of genes. Then, based on the learned temporal representations of genes, we develop a statistical method to construct a series of dynamic TRNs associated with the development of specific phenotypes. Finally, we identify the phenotype-specific TFs and pathways from the constructed dynamic TRNs. Results from multiple phenotypic datasets show that the proposed DTGN framework outperforms most existing methods in identifying phenotype-specific TFs and pathways. Our framework offers a new approach to exploring the functional roles of transcription regulators that associate with specific phenotypes in a dynamic model.

BiLSTM-Filt: Neural network for radar word segmentation.

Radar word extraction is the analysis foundation for multi-function radars (MFRs) in electronic intelligence (ELINT). Although neural networks enhance performance in radar word extraction, current research still faces challenges from complex electromagnetic environments and unknown radar words. Therefore, in this paper, we propose a promising two-stage radar word extraction framework, consisting of segmentation and recognition. To fill the vacancy of radar word segmentation, we establish the mathematical model from the time series analysis viewpoint and design a novel segmentation neural network based on Bi-direction Long Short-Term Memory with a filter module (BiLSTM-Filt). Specific radar word structure characteristics are extracted by training the network and applied for detecting radar words in the pulse train. To further improve segmentation performance, a bounding box regression method is designed to merge information from sub-region structures. Simulation experiments on a typical MFR, Mercury, reveal that the proposed method can outperform the baseline methods within complex electromagnetic environments, containing corrupted environments, various pulse backgrounds, and variable pulse train lengths. Due to the artificial design structure, the proposed method can also make a trial on unknown radar word segmentation.

Quantifying Hand Motion Complexity in Simulated Sailing Using Inertial Sensors.

The control of hand movement during sailing is important for performance. To quantify the amount of regularity and the unpredictability of hand fluctuations during the task, the mathematical algorithm Approximate Entropy (ApEn) of the hand acceleration can be used. Approximate Entropy is a mathematical algorithm that depends on the combination of two input parameters including (1) the length of the sequences to be compared (m), and (2) the tolerance threshold for accepting similar patterns between two segments (r). The aim of this study is to identify the proper combinations of 'm' and 'r' parameter values for ApEn measurement in the hand movement acceleration data during sailing. Inertial Measurement Units (IMUs) recorded acceleration data for both the mainsail (non-dominant) and tiller (dominant) hands across the X-, Y-, and Z-axes, as well as vector magnitude. ApEn values were computed for 24 parameter combinations, with 'm' ranging from 2 to 5 and 'r' from 0.10 to 0.50. The analysis revealed significant differences in acceleration ApEn regularity between the two hands, particularly along the Z-axis, where the mainsail hand exhibited higher entropy values (p = 0.000673), indicating greater acceleration complexity and unpredictability. In contrast, the tiller hand displayed more stable and predictable acceleration patterns, with lower ApEn values. ANOVA results confirmed that parameter 'm' had a significant effect on acceleration complexity for both hands, highlighting differing motor control demands between the mainsail and tiller hands. These findings demonstrate the utility of IMU sensors and ApEn in detecting nuanced variations in acceleration dynamics during sailing tasks. This research contributes to the understanding of hand-specific acceleration patterns in sailing and provides a foundation for further studies on adaptive sailing techniques and motor control strategies for both novice and expert sailors.

Time series analysis of comprehensive maternal deaths in Brazil during the COVID-19 pandemic.

The effects of the COVID-19 pandemic on comprehensive maternal deaths in Brazil have not been fully explored. Using publicly available data from the Brazilian Mortality Information (SIM) and Information System on Live Births (SINASC) databases, we used two complementary forecasting models to predict estimates of maternal mortality ratios using maternal deaths (MMR) and comprehensive maternal deaths (MMRc) in the years 2020 and 2021 based on data from 2008 to 2019. We calculated national and regional standardized mortality ratio estimates for maternal deaths (SMR) and comprehensive maternal deaths (SMRc) for 2020 and 2021. The observed MMRc in 2021 was more than double the predicted MMRc based on the Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per 100,000 live births, respectively). We found persisting sub-national variation in comprehensive maternal mortality: SMRc ranged from 1.74 (95% confidence interval [CI] 1.64, 1.86) in the Northeast to 2.70 (95% CI 2.45, 2.96) in the South in 2021. The observed national estimates for comprehensive maternal deaths in 2021 were the highest in Brazil in the past three decades. Increased resources for prenatal care, maternal health, and postpartum care may be needed to reverse the national trend in comprehensive maternal deaths.

Transcriptomic analysis reveals Streptococcus agalactiae activation of oncogenic pathways in cervical adenocarcinoma.

Cervical adenocarcinoma (AC), a subtype of uterine cervical cancer (CC), poses a challenge due to its resistance to therapy and poor prognosis compared with squamous cervical carcinoma. Streptococcus agalactiae [group B Streptococcus (GBS)], a Gram-positive coccus, has been associated with cervical intraepithelial neoplasia in CC. However, the underlying mechanism interaction between GBS and CC, particularly AC, remains elusive. Leveraging The Cancer Genome Atlas public data and time-series transcriptomic data, the present study investigated the interaction between GBS and AC, revealing activation of two pivotal pathways: 'MAPK signaling pathway' and 'mTORC1 signaling'. Western blotting, reverse transcription-quantitative PCR and cell viability assays were performed to validate the activation of these pathways and their role in promoting cancer cell proliferation. Subsequently, the present study evaluated the efficacy of two anticancer drugs targeting these pathways (binimetinib and ridaforolimus) in AC cell treatment. Binimetinib demonstrated a cytostatic effect, while ridaforolimus had a modest impact on HeLa cells after 48 h of treatment, as observed in both cell viability and cytotoxicity assays. The combination of binimetinib and ridaforolimus resulted in a significantly greater cytotoxic effect compared to binimetinib or ridaforolimus monotherapy, although the synergy score indicated an additive effect. In general, the MAPK and mTORC1 signaling pathways were identified as the main pathways associated with GBS and AC cells. The combination of binimetinib and ridaforolimus could be a potential AC treatment.

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.

Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023.

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

COVID-19 Pandemic and Hospitalizations due to Abortion among 10- to 14-Year-Old Girls in Brazil.

Introduction: Various non-pharmacological interventions to prevent coronavirus dissemination were implemented during the COVID-19 pandemic, including school closures. The effect of these interventions on particular aspects of people's lives such as sexual and reproductive health outcomes has not been adequately discussed. The objective of the study was to compare the monthly hospital admission rates due to abortion before and during school closure. Methods: We used an interrupted time series (IES) design to estimate the hospital admission rates before and during the school closure (intervention in March 2020) period. The analysis was performed considering all girls from age groups of interest and by stratifying the age groups according to skin color (white and non-white) in which the non-white category comprised both the black and mixed ethnicity together. Coefficients and 95% confidence intervals (95% CIs) were calculated using segmented linear regression models. Results: The results showed positive and statistically significant coefficients, suggesting post-intervention trend changes both in the population as a whole (coefficient: 0.07; 95% CI: 0.02; 0.11) and the non-white population group (coefficient: 0.07; 95% CI: 0.03; 0.11), indicating that the monthly hospital admission rates increased over the post-intervention period compared to baseline pre-intervention period. The ITS analysis did not detect statistically significant trend changes (coefficient: 0.02; 95% CI: -0.01; 0.05) in abortion admission rates in the white girl population group. Conclusion: The hospitalizations in Brazil due to abortions in 10- to 14-year-old girls increased during the COVID-19 pandemic in 2020 compared to 2019, and the number of abortions was higher in the non-white population than the white population. Furthermore, recognizing that the implementation of school closure has affected the minority population differentially can help develop more effective actions to face other future similar situations.

A tale of two times: an exploration of healthcare utilization patterns before and during COVID-19 in Iran.

BACKGROUND: The COVID-19 pandemic has impacted global healthcare utilization patterns. This study aimed to examine the impact of COVID-19 pandemic on utilization rate of healthcare services in Iran. METHOD: In this quasi-experimental study, data on the utilization rates of laboratory services, sonography exams, CT scans, MRIs, and EKGs was collected from the Social Security Organization (SSO)'s insurance information system. This data, covering 47 months prior to the pandemic and 25 months during it, was analyzed to assess the pandemic's impact on healthcare utilization among insured individuals in Iran. The data was categorized into direct, indirect, and total sectors, and an Interrupted Time Series Analysis (ITSA) model was employed for data analysis, examining both total and sector-specific utilization rates. FINDINGS: The study for single group indicated that in the total sector, Utilization rate per 1000 insured significantly decreased by 25.25 for laboratory services, decreased by 3.99 for sonography, decreased by 1.08 for MRIs and decreased by 1.01 for EKGs, but increased by 2.28 for CT scans in the first pandemic month. Over following months, monthly utilization trends per 1000 insured increased significantly- laboratory services + 1.08, sonography + 0.11, CT scans + 0.12, MRIs + 0.06, and EKGs + 0.05. Pre-pandemic, monthly utilization per 1000 insured was 62.68 labs, 14.47 sonography, 0.72 CT scans, 2.06 MRIs, with all significantly higher in the indirect over direct sector except EKGs which were 2.08 higher in the direct sector. In the first pandemic month, there were significant between-sector differences per 1000 of -4.4 for sonography, + 1.89 CT scans, -1.01 MRIs and + 1.29 EKGs. CONCLUSION: The COVID-19 pandemic led to a significant decline in healthcare service utilization, particularly in total and direct sectors, while CT scans remained unaffected. To address these challenges and meet patient needs, Iran's health system should adopt alternative delivery methods like telemedicine.