Time Series Analysis Research Articles

Antibiotic spectrum coverage scoring as a potential metric for evaluating the antimicrobial stewardship team activity: a single-center study.

Days of antibiotic spectrum coverage (days of ASC: DASC) is a metric for antibiotic usage calculated by ASC scores for spectrum and addresses limitations of days of therapy (DOT), which does not include spectrum. This study aims to investigate whether ASC-related metrics offer different aspects compared to aggregated DOT for all antibiotics (DOTtotal) and to assess their correlation in evaluating the impact of antimicrobial stewardship team (AST) programs. Retrospective. A single center within an 845-bed hospital. Trends in DOTtotal, DASC, and the DASC/DOT ratio, representing the average spectrum coverage per therapy day, were analyzed pre- and post-AST programs (April 2018) from January 2015 to December 2023, using interrupted time series analysis. Independent of the DASC/DOT, we also advocated ASC-stratified DOT (ASDOT), which facilitates comprehensive evaluation of DOT across ASC scores of <6, 6-10, and >10, representing narrow-, intermediate-, and broad-spectrum antibiotics. Among inpatients, AST programs significantly moderated the increasing trends of these metrics. Specifically, although the rates of increase in DOTtotal and DASC were slowed or plateaued, the DASC/DOT ratio decreased (P < 0.001). ASDOT metrics revealed a decrease and subsequent plateau in DOTtotal for the broad- and intermediate-spectrum antibiotics, with an increase observed for the narrow-spectrum antibiotics (P < 0.001 for each). DASC did not provide additional insights in the outpatient's population. The study demonstrates that ASC-related metrics may yield different and useful conclusions about the effectiveness of AST programs for inpatients.

Optimizing Retail Inventory Management Through Time Series Analysis

The purpose of this study is to investigate the application of time series analysis in the management of retail inventory, with a particular emphasis on the precision of forecasting as well as the applicability of ARIMA models and seasonal decomposition techniques. This study aimed to evaluate these models' effectiveness in improving the scientific and precise nature of inventory management. This was accomplished by using legitimate retail data. According to the findings, the ARIMA model is effective in managing data that is stable or does not exhibit seasonal patterns. On the other hand, the seasonal decomposition technique demonstrates strong forecasting accuracy in managing data that exhibits seasonal fluctuations. Not only does this research improve the theoretical implementation of time series analysis in the retail sector, but it also provides retailers with practical strategies for mitigating economic losses that are caused by inventory imbalance. When it comes to the management of retail inventory, the findings of the research are significant for improving both overall cost control and customer satisfaction.

Impact of Average Atmospheric Temperature on Rainbow Trout Production in South Africa: A Time Series Analysis Approach

Climate change has various effects on the global food supply and the environment as a whole. Similarly, the production of fish like Rainbow Trout is affected by variation in the atmospheric temperature higher than 21oC, leading to fluctuations in yield. Therefore, this study sought to investigate the impact of average atmospheric temperature on Rainbow Trout production in South Africa using time series data spanning from 2000 to 2022. The unit root test, Johannsen cointegration test, and the Vector Error Correction model were employed to analyse the data. The results show that the change in average atmospheric temperature affects the production of Rainbow Trout. The correlation between average temperature and Rainbow Trout production is positive in the short-run and long-run. I Prolonged exposure to high temperature may lead to fish stress, migration and increased susceptibility to diseases which ultimately affects the economic viability of the sector in both the short-run and the long-run. In addition, the overall change in the atmospheric temperature has a negative effect on the production of the fish highlighting the need for climate change intervention to sustain fish production. The study recommends implementing strategies that reduce the impact of temperature changes on fish production to support both economic growth and food security.

Interventional Pharmacoepidemiology: Origins, Current Status, and Future Possibilities.

Interventional pharmacoepidemiology applies quantitative analysis of patterns of medication use and outcomes to help design, guide and then evaluate programs to improve prescription drug use and outcomes. Surveillance of prescribing and drug-taking in large populations is increasingly practical because of the proliferation of detailed data on medication use decisions, often based on paid claims billing data. At the same time, increasingly granular clinical information is available on patient characteristics and outcomes. This can offer important opportunities to identify problematic use, focus interventions to address them, and measure their impact. Alexander et al (Am J Epidemiol. 0000;000(00):0000-0000) review the need for such research and provide methodological guidance for its performance. While randomized controlled trials of such interventions are ideal, real-world considerations often require other evaluation strategies, including stepped-wedge designs and interrupted time-series analysis. As drug therapy becomes more powerful and more costly, and the risks of poor medication choices as well as under-use of effective treatments become even better understood, the health care system will increasingly rely on such approaches to assess current patterns of prescribing and patient adherence, target programs to address problem areas, and measure the effectiveness of such interventions.

Satellite measurement of forest disturbance, recovery and deposit distribution following explosive volcanic eruptions

The characteristics and extent of forest damage, and the subsequent patterns of recovery, reflect the intensity of an explosive volcanic eruption and have the potential to be a novel proxy for eruption magnitude and impact. Using satellite measurements of vegetation damage and recovery patterns, following the 2015 explosive eruption of Calbuco, Chile, we assess the impact on surrounding temperate forests and how areas impacted by different deposit types recover post-eruption. The Calbuco eruption resulted in tephra deposition over hundreds of square kilometres, pyroclastic flows extending 6 km and lahars extending 15 km. We explore NDVI derived from optical imagery (June 2013–May 2023) as well as radar backscatter and phase coherence (October 2014–June 2023) through time series analysis, clustering and estimation of recovery timescales to find patterns in forest disturbance and recovery. We find that forest damage and recovery correspond primarily with deposit type, thickness and dispersal directions. The thickest tephra deposits (> 40 cm) correlate with the most vegetation loss, so our vegetation impact maps allow us to refine the spatial mapping of tephra fall-deposit isopachs to give a revised eruption volume of 0.28 km3. Vegetation recovery rates relate to initial impact type and intensity, but also local topography, aspect and altitude. Our results demonstrate a novel application of optical and radar satellite remote sensing to determine eruption extents and magnitudes through vegetation disturbance. We show that measuring vegetation disturbance, particularly in remote and densely vegetated environments, can help refine field-based analyses in inaccessible or intensely damaged zones.

Rainfall events and daily mortality across 645 global locations: two stage time series analysis

ObjectiveTo examine the associations between characteristics of daily rainfall (intensity, duration, and frequency) and all cause, cardiovascular, and respiratory mortality.DesignTwo stage time series analysis.Setting645 locations across 34 countries or regions.PopulationDaily...

Spatiotemporal patterns and drivers of extreme fire severity in Spain for the period 1985–2018

Extreme fire severity patterns driven by climate change have deep consequences on vegetation, subsequent fire regimes and have consequences on the effectiveness of extinction. In this work, we aimed to determine if extreme fire severity defined as the 90th percentile of RdNBR has changed in Spain in the last 3 decades, if extreme fire severity patterns have differed among the different vegetation types and what drivers have influenced extreme fire severity in both summer and non-summer fires. We have used perimeters of large wildfires (≥100 ha, n = 1719) from 1985 to 2018 in four pyroregions and the main dominant vegetation types. We have analyzed time series for each pyroregion and vegetation type to determine if there were significant trends and breakpoints in extreme fire severity. We have also analyzed the effects of drought, weather, topography and vegetation on extreme fire severity in seasonal wildfires.Extreme fire severity increased in summer fires from 1985 to 2018, especially in the East and North pyroregions, while the Southwest pyroregion reached highest values in summer. There has been an increase of severity in shrubland, mosaic vegetation and conifer forests in summer. Broadleaved forests had the lowest values of fire severity and did not show any trend in the period considered. The most important drivers of extreme fire severity are, in order of importance, pyroregion, elevation, slope, vapor pressure deficit (VPD) and vegetation type for summer fires, and VPD, vegetation types, pyroregion, windspeed and drought for non-summer fires. Global change is increasing climate-related events. Our results indicate that extreme fire severity in summer has been on the rise in recent decades in Spain, suggesting that, in a climate warming context, without strategic management of these increasingly flammable landscapes, further increases may be likely.

Applications of Independent Component Thermography for Testing and Evaluation of Glass Fibre Reinforced Polymer Materials

Abstract A novel post-processing technique is proposed for analysing time series thermographic data obtained by imposing a frequency-modulated heat flux on a Glass Fiber Reinforced Polymer (GFRP) material for Non-Destructive Testing and Evaluation (NDT&amp;E). The proposed approach bridges the gap between the statistical and spatiotemporal analysis of the captured thermographic sequence to inspect and identify the flat bottom holes in the sample. It emphasizes the defect detection capabilities of the Principal Component Analysis (PCA) based thermography named Principal Component Thermography (PCT) and Independent Component Analysis (ICA) based thermography named Independent Component Thermography (ICT) and compares them by using two distinct algorithmic implementations for each method. The effectiveness of these four algorithmic implementations is evaluated using the dynamic range of the temporal profiles. This work presents a significant step toward gaining deeper insight into statistical post-processing techniques for defect identification in InfraRed Thermography (IRT).

Insights into Boreal Forest Disturbance from Canopy Stability Index

The world’s forests are being increasingly disturbed from exposure to the compounding impacts of land use and climate change, in addition to natural disturbance regimes. Boreal forests have a lower level of deforestation compared to tropical forests, and while they have higher levels of natural disturbances, the accumulated impact of forest management for commodity production coupled with worsening fire weather conditions and other climate-related stressors is resulting in ecosystem degradation and loss of biodiversity. We used satellite-based time-series analysis of two canopy indices—canopy photosynthesis and canopy water stress—to calculate an index that maps the relative stability of forest canopies in the Canadian provinces of Ontario and Quebec. By drawing upon available spatial time-series data on logging, wildfire, and insect infestation impacts, we were able to attribute the causal determinants of areas identified as having unstable forest canopy. The slope of the two indices that comprise the stability index also provided information as to where the forest is recovering from prior disturbances. The stability analyses and associated spatial datasets are available in an interactive web-based mapping app. that can be used to map disturbed forest canopies and the attribution of disturbances to human or natural causes. This information can assist decision-makers in identifying areas that are potentially ecologically degraded and in need of restoration and those stable areas that are a priority for protection.

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

THE APPLICATION OF SPIRITUAL COUNSELING TO OVERCOME GENERALIZED ANXIETY DISORDER (GAD): A CASE STUDY ON CLIENT “I” FROM GOMBAK, SELANGOR, MALAYSIA

Generalized Anxiety Disorder (GAD) is a mental disorder involving excessive and uncontrollable fear, anxiety, and worry. Referring to this research, the focus of the study is related to the description of client “I”’s GAD, such as factors, symptoms, and level of GAD anxiety disorders. This research also explains how the spiritual counseling approach can overcome client “I” ‘s GAD problem based on Surah Al-Ma'arij from verses 19 to 32. Client “I” is the main source for this research. Therefore, this research was carried out in three meetings from 03 September 2023 to 10 January 2024 according to the type of research using field research with qualitative methods. The data from this research were obtained from interviews and observations and used data analysis for case studies that had been developed by Robert K. Yin, namely pattern matching, explanation, and time series analysis. The results of the study showed that there was a significant impact on the symptoms of anxiety disorder in a client “I”. Apart from that, the GAD level of client “I” also showed a positive change from a score of 14 to a score of 11 using the GAD-7 Rating Scale. In conclusion, a systematic spiritual counseling approach guided by the verses of the Qur'an has been proven to bring positive changes and overcome Generalized Anxiety Disorder (GAD).

A time-series analysis examining implementation strategies to increase use of an early-supported discharge hospital at home model.

Early-supported discharge (ESD) hospital-at-home (HaH) programs facilitate hospitalized patients to receive ongoing acute-level care at home, thereby promoting patient-centeredness while improving hospital throughput. The current study aimed to test multiple implementation strategies to increase and sustain HaH ESD utilization. We conducted interrupted time series analyses to evaluate the effectiveness of implementation strategies on weekly HaH ESD referrals and capacity utilization at five hospitals. Intervention 1 included provider-focused education and HaH nurse navigator support (July 2021 to May 2022). Intervention 2 added provider-level referral performance feedback and daily electronic health record-based eligibility reports (May 2022 to December 2022). During postintervention (January 2023 to June 2023), implementation strategies were no longer supported by the study team. Clinical outcomes were assessed over time and between patient subgroups. There were 5951 HaH ESD patients overall. After Intervention 2, we observed immediate increases in weekly HaH ESD referrals (level change mean difference [MD, 95% confidence interval]: 14.8, 5.9-23.6) and capacity utilization (level change MD: 13.9%, 6.2%-21.5%) and additional week-to-week increases in capacity utilization (slope change MD: 0.6%, 0.2%-0.9%), compared to Intervention 1 trends. HaH ESD referrals and capacity utilization were sustained postintervention. The proportion of provider-initiated referrals increased over time (Intervention 1: 79.4%, Intervention 2: 90.9%, postintervention: 95.2%). As HaH ESD utilization increased, we observed shorter inpatient length of stay and fewer HaH ESD encounters per visit (p < 0.01). There were small, statistically significant differences in 30-day mortality and readmission for residents of rural and socioeconomically disadvantaged areas. Applying referral-focused provider feedback and daily eligibility reports were effective within a multicomponent approach to increase and sustain HaH ESD utilization.

Temporal trend and epidemiological profile of accidents involving venomous animals in the State of Rio de Janeiro, Brazil (2012-2021)

Introduction: Accidents involving venomous animals have significant clinical implications. In Brazil, accidents caused by scorpions (59%), spiders (13.6%), and snakes (10.9%) are the mostfrequently reported, although proportions vary by region. This study describes the temporal trends and epidemiological characteristics of accidents involving venomous animals in the State of Rio de Janeiro. Methods: This is an ecological (time series) study of accidents caused by venomous animals over the last 10 years (2012 to 2021) in Rio de Janeiro. Data were collected from the Brazilian Notifiable Diseases Information System. We estimated incidence rates and analyzed time series using Joinpoint regression, according to geographical region, age, and type of animal. Results: From 2012 to 2021, 17,891 accidents involving venomous animals were reported, with rates increasing from 8.4 to 10.7 per 100,000 inhabitants. Snakes were responsible for the highest number of cases (33.4%), followed by scorpions (27.2%) and spiders (27.0%). Snakebite cases remained stable, while cases of arachnidism and scorpion envenomation increased, marking them as the main types of accidents. The regions with the highest rates were Centro-Sul (65.5 per 100,000 inhabitants), Baía da Ilha Grande (62.3), Médio Paraíba (45.3), and Serrana (43.5). Males and the 40 to 59 age group predominated, but all age groups showed an increase, with a more pronounced rise among children and adolescents. Most accidents were classified asmild, although 51 deaths were recorded.Conclusions: Rio de Janeiro has relatively low rates of accidents involving venomous animals,but some regions have values higher than the national average. Snakebite accidents remainedstable, except in the adolescent group. Accidents involving scorpions and spiders showed anupward trend.

Automatic Enrollment in Patient Portal Systems Mitigates the Digital Divide in Healthcare: An Interrupted Time Series Analysis of an Autoenrollment Workflow Intervention

PurposeRacial and ethnic healthcare disparities require innovative solutions. Patient portals enable online access to health records and clinician communication and are associated with improved health outcomes. Nevertheless, a digital divide in access to such portals persist, especially among people of minoritized race and non-English-speakers. This study assesses the impact of automatic enrollment (autoenrollment) on patient portal activation rates among adult patients at the University of California, San Francisco (UCSF), with a focus on disparities by race, ethnicity, and primary language.Materials and methodsStarting March 2020, autoenrollment offers for patient portals were sent to UCSF adult patients aged 18 or older via text message. Analysis considered patient portal activation before and after the intervention, examining variations by race, ethnicity, and primary language. Descriptive statistics and an interrupted time series analysis were used to assess the intervention’s impact.ResultsAutoenrollment increased patient portal activation rates among all adult patients and patients of minoritized races saw greater increases in activation rates than White patients. While initially not statistically significant, by the end of the surveillance period, we observed statistically significant increases in activation rates in Latinx (3.5-fold, p = < 0.001), Black (3.2-fold, p = 0.003), and Asian (3.1-fold, p = 0.002) patient populations when compared with White patients. Increased activation rates over time in patients with a preferred language other than English (13-fold) were also statistically significant (p = < 0.001) when compared with the increase in English preferred language patients.ConclusionAn organization-based workflow intervention that provided autoenrollment in patient portals via text message was associated with statistically significant mitigation of racial, ethnic, and language-based disparities in patient portal activation rates. Although promising, the autoenrollment intervention did not eliminate disparities in portal enrollment. More work must be done to close the digital divide in access to healthcare technology.

Measles and Pertussis outbreaks in England and Wales: a time-series analysis

Background Vaccine coverage for common infectious diseases such as Measles and Pertussis (also known as whooping cough) have been declining in England and Wales since 2014. Consequently, significant increases in Measles and Pertussis cases are observed in the community. Aim To explore whether Google Trends offers a predictive utility as a health surveillance tool for Meases and Pertussis in England and Wales. Design and Setting Google search data related to Measles and Pertussis, including common associated symptoms, were downloaded for 52 weeks from 07/01/2023 – 07/01/2024. Measles and Pertussis case data were retrieved from the weekly Notification of Infectious Disease (NOID) reports. Methods The associations between searching and case data were explored using a time-series analyses, including cross-correlations, Prais-Winsten regression and joinpoint analysis. Results Significant cross-correlations were found for Measles cases and “measles” searching (r=.41) at a lag of -1 week. For Pertussis cases, searching for “whooping cough” (r=.31), “cough” (r=.39), “100 day cough” (r.41) and “vomiting” (r=.42) were significantly correlated at a lag of -3 to -2 weeks. In multivariable regression, “measles” remained significantly associated with Measles cases (β=.24, SE=.33, p=.02) as did “whooping cough” (β=.71, SE=.27, p=.01) and “cough” (β=1.99, SE=.54, p=.001) for pertussis. Conclusion Increases in Measles and Pertussis cases follow increases in online searches for both diseases and selected respective symptoms. Further work is required to explore how GT can be used in conjunction with other health surveillance systems to monitor or even predict disease outbreaks, to better target public health interventions.

Improving Power Spectrum Estimation Using Multitapering: Efficient Asteroseismic Analyses for Understanding Stars, the Milky Way, and Beyond

Asteroseismic time series data have imprints of stellar oscillation modes, whose detection and characterization through time series analysis allows us to probe stellar interior physics. Such analyses usually occur in the Fourier domain by computing the Lomb–Scargle (LS) periodogram, an estimator of the power spectrum underlying unevenly sampled time series data. However, the LS periodogram suffers from the statistical problems of (1) inconsistency (or noise) and (2) bias due to high spectral leakage. Here, we develop a multitaper power spectrum estimator using the nonuniform fast Fourier transform (mtNUFFT) to tackle the inconsistency and bias problems of the LS periodogram. Using a simulated light curve, we show that the mtNUFFT power spectrum estimate of solar-like oscillations has lower variance and bias than the LS estimate. We also apply our method to the Kepler-91 red giant, and combine it with PBjam peakbagging to obtain mode parameters and a derived age estimate of 3.97 ± 0.52 Gyr. PBjam allows the improvement of age precision relative to the 4.27 ± 0.75 Gyr APOKASC-2 (uncorrected) estimate, whereas partnering mtNUFFT with PBjam speeds up peakbagging thrice as much as LS. This increase in efficiency has promising implications for Galactic archaeology, in addition to stellar structure and evolution studies. Our new method generally applies to time-domain astronomy and is implemented in the public Python package tapify, available at https://github.com/aaryapatil/tapify.

Ecological and epidemiological characteristics of tick-borne encephalitis and ixodic tick-borne borreliosis incidence according to the data of the Arkhangelsk region for 2012–2022

BACKGROUND: The increase of ticks infected with borrelia and tick-borne encephalitis virus, as well as the impact of anthropogenic factor on the ecosystem of the northern region determines the need to study possible risk factors and epidemiological trends in the incidence of tick-borne encephalitis and ixoid tick-borreliosis. AIM: To conduct a retrospective ecological and epidemiological analysis of regional peculiarities of the epidemic process of tick-borne encephalitis and ixoid tick-borreliosis in the Arkhangelsk region in comparison with similar indicators in the North-West Federal District and Russia for 2012–2022. MATERIALS AND METHODS: The work was carried out in accordance with the ecological research design. Statistical data of the Unified Interdepartmental Information and Statistical System, as well as statistical annual forms of the Department of the Federal Service for Supervision of Consumer Rights Protection and Human Welfare in Russia and the Arkhangelsk region were used as the information base. The registered cases of tick-borne encephalitis and ixoid tick-borreliosis morbidity in the Arkhangelsk region, as well as in Russia and the Northwestern Federal District as a whole for 2012–2022 were evaluated. Time series analysis was performed using Microsoft Excel 2019. The study also analyzes the dynamics of climatic factors in the Arkhangelsk region and its possible impact on the distribution and migration of ixodid ticks across the territory of the European North of Russia. Correlations were assessed by evaluating Pearson’s correlation criterion using the Google Collaboratory interface. RESULTS: The number of tick bites in the Arkhangelsk region throughout the entire study period significantly exceeds the similar indicator (per 100 thousand population) in Russia. The incidence of tick-borne encephalitis in the Arkhangelsk region significantly exceeds that in both the Russian Federation and the Northwestern Federal District, and exceeded the average annual rate in Russia more than 3 times, although it had a faster rate of decline (5.7% vs. 3.7% annually). The incidence of ixoid tick-borreliosis has a paradoxical character due to the fact that throughout almost the whole period it is below the all-Russian level (3.19 per 100,000 population in Arkhangelsk region against 4.41 in the Russian Federation), while the incidence in the Northwestern Federal District is 1.3 times higher on average. CONCLUSIONS: During the last decade the incidence of tick-borne encephalitis in the Arkhangelsk region remains stably high in comparison with both the Russian Federation and the Northwestern Federal District. The incidence of ixoid tick-borreliosis corresponds to the long-term average for Russia, with a more pronounced downward trend (4.7% vs. 1.7%). The territory of the Arkhangelsk region is subject to the trend of increasing average annual temperatures, and anthropogenic interventions, in particular, abundant deforestation, lead to the destruction of established ecosystems and open new opportunities and territories for ticks, which in the future may negatively affect the established trends of morbidity. The analysis confirms the need to develop and adopt organizational measures aimed at the development of epidemiological and epizootological monitoring.

The Impact of Agricultural Added Value, Trade Openness and Inflation on FDI: A Time Series Analysis from Saudi Arabia

The Impact of Agricultural Added Value, Trade Openness and Inflation on FDI: A Time Series Analysis from Saudi Arabia

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

The study aims to explore whether short-term exposure to meteorological factors has a potential association with the risk of diabetes mellitus (DM) mortality. 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. 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. 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.

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

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). 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. 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. 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.