Time Series Regression Models Research Articles

3-D Localization of RFID Tags Using SA Single Antenna Based on Time-Series Regression Model

3-D Localization of RFID Tags Using SA Single Antenna Based on Time-Series Regression Model

Demographic and Geographic Characteristics Associated with the Type of Prescription and Drug Expenditure: Real World Evidence for Greece During 2015-2021.

Electronic prescribing has allowed for the collection of prescription data in real time in Greece for the first time. Hence, the aim of the current study was to present the characteristics of prescriptions for the Greek population during the period from 2015 to 2021. This retrospective study was based on data extracted from the nationwide Greek electronic prescription database between January 2015 and December 2021. Descriptive statistics methods were used for the needs of the study. As the basic figures examined depend on the size of the population, in order for the results to be comparable, we estimated the corresponding measures per inhabitant, using population data from the Greek Statistical Authority. Appropriate indicators for the comparison of consumption and expenditure over time were estimated. A study of the trend was also carried out using time series and linear regression models. In order to facilitate the design and implementation of specialized policies, it is useful to identify the drug categories with the highest consumption and expenditure, as well as the geographical areas that present similar characteristics. For the first, ABC analysis was used, which helps to identify the most popular categories of drugs, while for the second, cluster analysis was carried out. Agglomerative clustering was used to divide the regions into similar groups. This hierarchical clustering algorithm classifies the population into several clusters, with areas in the same cluster being more similar, and areas in different clusters being dissimilar. The Ward linkage method with Euclidean distance was used. The analysis of prescription drug consumption and expenditure from 2015 to 2021 revealed significant fluctuations and trends across various drug categories, age groups, and geographical areas. Notably, the quantity of prescriptions increased by 20% since 2015, while expenditure surged by over 30%, with significant spikes following the end of the MoU in 2019 and the onset of the pandemic in 2020. In terms of expenditure, antineoplastic and immunomodulation agents (category L) held the largest share, driven by the introduction of new, costly drugs. The expenditure per inhabitant revealed gender and age disparities, with older populations, particularly women, incurring higher costs. Geographically, drug expenditure, and consumption varied significantly, with distinct regional clusters identified. These clusters, while showing some overlap in consumption and expenditure patterns, also highlighted unique regional characteristics. The insights into prescription drug consumption and expenditure trends offer a valuable basis for developing targeted interventions aimed at optimizing healthcare resource allocation. Moreover, the findings underscore the importance of addressing regional and demographic disparities in pharmaceutical use, thereby contributing to more equitable and cost-effective healthcare strategies. More specifically, the age distribution of prescriptions shows the increase in younger ages, which, as a result, anticipates the overall increase in prescriptions. The knowledge of the most convex categories of medicine, as well as the percentages of the use of generic drugs, shows where interventions should be made, with financial incentives and information through new information channels. The geographic disparities recorded should lead to policies that help the residents of hard-to-reach areas to access prescriptions. In addition, the present study provides a strategic framework for policymakers and healthcare managers to guide future studies and inform decision-making processes.

Impact of the COVID-19 pandemic on incidence of coronary heart disease in Bavaria, Germany: an analysis of health claims data

BackgroundInconsistent findings about the impact of the COVID-19 pandemic on cardiovascular disease diagnosis and consultations have been reported internationally. The objective of this study was to analyse the impact of...

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Time taken for cardiology care and one-year results for primary care physicians' referrals for cardiology consultation in heart failure patients: clinical implications for heart failure management

Abstract Background and Purpose The clinical trajectory of patients with heart failure (HF) is characterised by frequent episodes of decompensation requiring urgent medical assistance, hospitalisations, and an increased risk of mortality. HF clinical guidelines recommend early visits by health professionals after hospital discharge, yet little evidence exists regarding the associations between delayed medical attention and outcomes in HF patients. To assess the clinical impact of the time taken to receive cardiology care for referrals from primary care physicians (PCPs) to a cardiology department in HF patients, we selected 6,859 HF patients who visited the cardiology service at least once between 2010 and 2021. Methods Using an interrupted time series regression model, we analysed the impact of integrating e-consultations into the healthcare model (implemented in 2013) and evaluated the time taken to receive cardiology care, as well as all-cause, cardiovascular (CV), and HF-related hospital admissions and mortality. The primary objective of this analysis was to examine the associations between delays in cardiology care following a PCP referral and 1-year outcomes (HF-related, cardiovascular, and total hospitalisations; and HF-related, cardiovascular, and total mortality). Results Almost 50% were women, and the mean age was 77.8 years. Arterial hypertension (79.6%), diabetes (34%), atrial fibrillation (51.4%), ischemic heart disease (20.4%), and cerebrovascular disease were the most prevalent comorbidities. The mean time from PCP referral to cardiology consultation in the overall population was 22.3 days: <8 days in 53.9%, 8-14 days in 17.1%, 15-30 days in 15.9%, and >30 days in 13.0% of patients, respectively. One year after cardiology consultation, 68.6% of patients required assistance in an Emergency Department; 22.5% were hospitalised for any cause, 14.9% for cardiovascular reasons, and 9.2% for HF-related issues. Moreover, total mortality at 1-year was 8.6%, comprising 4.3% cardiovascular mortality and 1.3% HF-related deaths. Multivariate analysis of the relationship between the time taken to receive cardiology care and 1-year outcomes revealed a significant increased risk associated with a longer delay in cardiology assistance, particularly evident in the subgroup of patients with previous hospital admissions due to worsening HF (see Table). Conclusions Throughout the follow-up period, a reduced time taken to receive care was independently associated with better 1-year outcomes. We believe that our experience can contribute to the development of a more efficient ambulatory care pathway for HF patients, especially those at high risk, such as those with a history of hospitalisation due to worsening events. Our findings further support the clinical guideline recommendation for early contact by health professionals following hospital discharge, extending it to include PCP referrals for cardiology consultations.

Impact of a clinician-to-clinician electronic consultation program in heart failure patients with previous hospitalization. Implications for heart failure multidisciplinary management

Abstract Background A clinician-to-clinician electronic consultation (e-consultation) programme may not only improve the accessibility to care but may also impact patient outcomes, particularly in heart failure (HF) patients with a previous episode of hospitalization (HFH), group of patients associated with a worse outcome. Purpose To evaluate the impact of an outpatient care management programme that includes a clinician-to-clinician e-consultation on delay time in care, hospital admissions, and mortality in a high-risk group of patients with heart failure (HF) and previous episodes of HF hospitalization (HFH). Materials and methods We selected 6444 HF patients who visited the cardiology service at least once between 2010 and 2021. Of these, 4146 were attended in e-consultation, and 2230 had previous HFH (Fig 1a). Using an interrupted time series regression model, we analysed the impact of incorporating e-consultation into the healthcare model in the group of patients with HFH and evaluated the elapsed time to cardiology care, HF, cardiovascular (CV), and all-cause hospital admissions and mortality, calculating the incidence relative risk (iRR). We performed a multivariate logistic regression for each of these outcomes in both groups. Results Patients with HFH had a higher prevalence of men (P < 0.001) but had a similar age (P = 0.267) compared to patients without HFH. Patients with HFH had a higher prevalence of diabetes (P < 0.001), ischaemic heart disease (P = 0.036), and peripheral arterial disease (P = 0.008) (Fig 1b). In the group of patients with HFH, the introduction of e-consult substantially decreased waiting times to cardiology care (8.6 [8.7] vs. 55.4 [79.9] days, P < 0.001) (Fig 2a). In that group of patients, after e-consult implantation, hospital admissions for HF were reduced (iRR [95%CI]: 0.837 [0.840–0.833]), 0.900 [0.862–0.949] for CV and 0.699 [0.678–0.726] for all-cause hospitalizations (Fig 2b). There was also lower mortality (iRR [95%CI]: 0.715 [0.657–0.798] due to HF, 0.737 [0.764–0.706] for CV and 0.687 [0.652–0.718] for all-cause) (Fig 2c). The improved outcomes after e-consultation implementation were significantly higher in the group of patients with previous HFH. The multivariate analyses showed a higher risk of hospitalizations in men, patients with HFH, and those who required more emergency department assistance (Fig 2d). Conclusions In patients with HFH, an outpatient care programme that includes an e-consultation significantly reduced waiting times to cardiology care and was safe, with a lower rate of hospital admissions and mortality in the first year. Our results may have implications for optimize the heart failure care organization.Figure 1Figure 2

Electronic consultation from primary care to a cardiology department improves outcomes in patients with cancer

Abstract Background The number of cancer patients with known cardiovascular (CV) disease is increasing due to improved cancer prognosis. Purpose The objective of this study is to assess the long-term results of cancer patients referred to a cardiology department by primary care using electronic consultation (e-consultation). Methods We analysed the patients with previous diagnosis of cancer referred to a cardiology department (CD) from 2010 to 2021 (n=6.889), selected from a total of 68.518 patients (61.629 without cancer), and compared two assistance models: in-person practice (from 2010 to 2012) and e-consultation (from 2013 to 2021). In the first period, all the patients referred by primary care physicians (PCPs) were seen in an in-person consultation and in the e-consultation model the cardiologist reviewed the electronic records (e-consultation) and decided to solve without any visit or, if it was necessary, with an in-person consultation. The outcomes between both periods were analysed using an interrupted time series regression model: 1) delay time to cardiology assistance; 2) all-cause and cardiovascular hospital admissions, and 3) all-cause and cardiovascular mortality during the first year after the initial consultation or e-consultation to the CD. Results Among cancer patients referred to cardiology consultation, the introduction of e-consultation decreased waiting times to cardiology care by 51,8% (95% CI: 51,7%-51,9%) (Fig 1). Over 60% of referrals were resolved in less than 8 days, with this proportion significantly higher after the e-consultation program was introduced (p<0,001). Furthermore, we observed a reduction in the one-year rate of outcomes, with an incidence rate ratio (iRR) [IC 95%] of 0,75 [0,73-0,77] for CV-related hospitalizations (Fig 2b), 0,43 [0,42-0,44] for all-cause hospitalization (Fig 2a), and 0,87 [0,86-0,88] for all-cause mortality. In the multivariate analysis, the delay in receiving cardiology care was significantly associated with an independent increase in 1-year mortality (OR [95% CI] 1,39 [1,06-1,83]) and hospital admission (OR [95% CI] 1,25 [1,02-1,52]). Furthermore, the implementation of the e-consult model was associated with a reduction in hospital admission rates (OR [95% CI] 0,19 [0,14-0,25]). Conclusions In comparison to the in-person consultation period, an outpatient care program that incorporates e-consultation for cancer patients significantly reduced waiting times for cardiology care and shown to be safe, associated with lower rates of hospital admissions.Figure 1Figure 2

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study.

Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries. Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates. The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively. Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.

Rising trend of dentist-prescribed antibiotics in Brazil: 2011-2021.

To describe the trends in antibiotic prescribing by dental practitioners and to investigate the relationship between these trends and some factors of public oral health services in Minas Gerais (MG), Brazil. This was a time-series analysis of antibiotics prescribed by dental practitioners between January 2011 and December 2021. The outcome variables were number of defined daily doses (DDD) and DDD/1000 population/year in a sample of cities in MG. Covariates were public oral healthcare factors, such as coverage, estimates of dental procedures, and frequency of dental pain. Linear time-series regression models were used to examine trends and the influence of covariates on antibiotic prescribing. Overall, the number of prescriptions increased by 334.69% between 2011 and 2021, with amoxicillin being the most commonly prescribed drug (78.53%). The number of DDD for all antibiotics increased from 17,147.13 to 77,346.67 and the average DDD/1000 inhabitants/year was 126.66 (SD: 130.28). The linear time-series regression model showed that for each one-year increase, the average log DDD/1000 inhabitants increased by 0.35 (standard error = 0.07, p < 0.001). No covariates were found to be associated with the outcome. In Minas Gerais, Brazil, a significant upward trend was observed in the number of prescriptions and the number of DDD of antibiotics prescribed by dental practitioners. No influence of factors related to public oral healthcare services on the outcome was observed, thereby emphasizing the need for further research on factors influencing medication use in dental practice.

Analysis of contact length and temperature effect in rocking mode diamond wire sawing of monocrystalline silicon carbide wafer

Diamond wire sawing (DWS) is a primary and fundamental stage for slicing large-diameter ingots into multiple wafers, enabling high-volume production in a single process. However, the extended contact length between the diamond wire and work material generates heat, which detrimentally impacts the surface quality of the sliced wafers and accelerates the diamond wire wear rate. This study implemented a rocking mode sawing strategy to investigate the effect of contact length on the surface quality of as-sawn wafer and diamond wire wear rate. Experiments have been conducted on monocrystalline silicon carbide (4H-SiC) with and without a rocking-mode multi-DWS machine. The experimental sawing temperature has been validated using Fourier’s law of thermal conduction, a finite element model, and a linear time series regression model. Results indicated that the minimal sawing temperature had been observed with the rocking mode sawing strategy, attributed to its shorter contact length compared to the process without rocking mode. Additionally, the finite element and regression models closely matched the experimental data, achieving accuracies of 93.57 % and 99.96 %, respectively. Fourier’s law of thermal conduction proved significant for precisely determining the sawing temperature. Notably, the rocking mode sawing strategy significantly affected the sawing temperature, surface quality, and diamond wire wear rate compared with the sawing process without the rocking mode.

Association between temperature and occupational injuries in Spain: The role of contextual factors in workers’ adaptation

BackgroundExtensive evidence links both cold and hot temperatures to an increased incidence of occupational injuries. Contextual modifiers of the temperature-injury association have been scarcely researched. The present study addresses temporal and spatial variations to identify factors associated with (mal)adaptation to heat and cold among Spanish workers. MethodsWe assessed the association between daily mean temperature and work injuries using quasi-Poisson time-series regression models in 48 Spanish provinces over the period 1988–2019, with comparative analyses with census and economic data for the sub-periods 1989–1993, 1999–2003, 2009–2013 and 2015–2019. We explored the spatial and spatiotemporal modification of the association by demographic and socioeconomic variables via cross-sectional and longitudinal meta-regressions. Findings:We found an increased risk of work-injuries by 4 % [95 % CI: 3 %-6 %] and 12 % [95 % CI: 10 %-13 %], for the 1st and 99th percentiles of temperature, respectively, for period 1988–2019. Heat had a greater overall impact than cold, and the groups more vulnerable to heat were male workers, under 35 years, and working in agriculture, construction and hostelry. Vulnerability to heat was highest in the earliest sub-period, while vulnerability to cold rose during periods of both economic expansion and recession. High educational attainment emerged as a protective factor during the warm months in the cross-sectional meta-regressions. ConclusionsOur findings suggest an adaptation of Spanish workers to high temperatures over time. However, preventive measures are needed for traditionally exposed workers (agriculture and construction), non-traditionally vulnerable sectors (hostelry), and young, male, and less educated workers during warm months. For cold vulnerability, targeted measures should focus on women, the elderly, and tertiary service workers, especially in colder regions. Addressing temperature vulnerability would enhance worker safety, reduce injuries, and yield economic benefits.

Effect of Temperature and Precipitation on Acute Appendicitis Incidence in Seoul: A Time Series Regression Analysis.

This study aimed to investigate the relationship between meteorological factors, specifically temperature and precipitation, and the incidence of appendicitis in Seoul, South Korea. Using data from the National Health Insurance Service spanning 2010-2020, the study analyzed 165,077 appendicitis cases in Seoul. Time series regression modeling with distributed-lag non-linear models was employed. Regarding acute appendicitis and daily average temperature, the incidence rate ratio (IRR) showed an increasing trend from approximately - 10°C to 10°C. At temperatures above 10°C, the increase was more gradual. The IRR approached a value close to 1 at temperatures below - 10°C and above 30°C. Both total and complicated appendicitis exhibited similar trends. Increased precipitation was negatively associated with the incidence of total acute appendicitis around the 50mm/day range, but not with complicated appendicitis. The findings suggest that environmental factors, especially temperature, may play a role in the occurrence of appendicitis. This research underscores the potential health implications of global climate change and the need for further studies to understand the broader impacts of environmental changes on various diseases.

A thirty-year time series analyses identifies coherence between oscillations in Anthrax outbreaks and El Niño in Karnataka, India

Anthrax is an economically important zoonotic disease affecting both livestock and humans. The disease is caused by a spore forming bacterium, Bacillus anthracis, and is considered endemic to the state of Karnataka, India. It is critical to quantify the role of climatic factors in determining the temporal pattern of anthrax outbreaks, so that reliable forecasting models can be developed. These models will aid in establishing public health surveillance and guide strategic vaccination programs, which will reduce the economic loss to farmers, and prevent the spill-over of anthrax from livestock to humans. In this study, correlation and coherence between time series of anthrax outbreaks in livestock (1987–2016) and meteorological variables and Sea Surface Temperature anomalies (SST) were identified using a combination of cross-correlation analyses, spectral analyses (wavelets and empirical mode decomposition) and further quantified using a Bayesian time series regression model accounting for temporal autocorrelation. Monthly numbers of anthrax outbreaks were positively associated with a lagged effect of rainfall and wet day frequency. Long-term periodicity in anthrax outbreaks (approximately 6–8 years) was coherent with the periodicity in SST anomalies and outbreak numbers increased with decrease in SST anomalies. These findings will be useful in planning long-term anthrax prevention and control strategies in Karnataka state of India.

Predicting GDP with Machine Learning Technique

This paper proposes a method for reducing model errors in regressions when modelling macroeconomic variables by using machine learning algorithms and traditional time series regression models. In this paper, machine learning models are subjected to repeated k-fold cross validation and hyperparameter tuning. The linear model uses repeated k-fold cross validation, on the other hand, the traditional time series model Mixed Data Sampling Auto Regressive Distribution Lag model is run without repeated k-fold cross validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the overall performance of machine learning algorithms and each model records the average outcome from all folds and runs. These findings demonstrate how machine learning models outperform the traditional time series model.

2020 CMS prior authorization for hospital outpatient departments: Associated surgical volume impact

BackgroundPrior authorization is common for privately administered Medicare Advantage plans but is rarely used for surgical care when considering publicly administered plans. A 2020 Centers for Medicare and Medicaid services (CMS) policy, CMS-1717-FC, requires prior authorization for Medicare Fee-for-Service beneficiaries undergoing select procedures (blepharoplasty, abdominoplasty, botulinum toxin injection, rhinoplasty, and vein ablation) in hospital outpatient departments. The impact of this policy on surgical volume at hospital outpatient departments and shifts in care to ambulatory surgery centers is unknown. MethodsThis study used a segmented interrupted time series and pre-post logistic regression model. This study was a retrospective cohort study using data from the Healthcare Cost and Utilization Project state ambulatory surgery database and state inpatient database. ResultsFrom 2016 through 2021, a total of 272,879 patients underwent the affected procedures. Pre–CMS-1717-FC, a trend of decreasing hospital outpatient department utilization was found for Medicare Fee-for-Service beneficiaries (–10.82, 95% confidence interval: –18.32 to –3.33, P = .01). In the post-implementation period, no change in the rate of decreasing hospital outpatient department utilization was found for Medicare Fee-for-Service beneficiaries (–3.45, 95% confidence interval: –36.15 to 29.25, P = .83). In the pre-policy period, Medicare Fee-for-Service beneficiaries were 46% less likely to use freestanding ambulatory surgery centers but 27% less likely to use hospital-owned ambulatory surgery centers. ConclusionCMS-1717-FC was not associated with significant changes in hospital outpatient department volume beyond baseline trends. Policy aiming to right-size prior authorization for these procedures and considering site-of-service will balance the need to ensure medical necessity while constraining costs.

Housing Price-Vacancy Dynamics—An Empirical Study of the Hong Kong Housing Market

This study uses time series regression models and dynamic panel models of five-class housing to investigate the dynamics of the housing price-vacancy relationship in Hong Kong, offering insights distinct from previous cross-sectional analyses that take new housing completions as a supply proxy, without considering vacant homes as a source of housing supply. Two major contributions emerge: first, the results support the hypothesis that housing vacancies exert a negative impact on housing prices, holding other factors constant. Second, new builds supply is found to have a positive effect on housing prices, which is in line with many previous studies, but it contradicts the prediction. The results challenge the use of land supply or new housing completions as the proxy of housing supply and put forward a novel suggestion of including vacant homes in the housing price analysis. Advanced approaches to collecting housing vacancy data are also discussed. These findings have significant implications for policymakers, urban planners, and real estate investors, providing valuable insights for crafting targeted interventions and informing investment decisions. This is one of the first time series and dynamic panel analyses of housing vacancy’s effect on prices.

Positive time series regression models: theoretical and computational aspects

Positive time series regression models: theoretical and computational aspects

Assessing two decades of landscape greenness in relation to temperature and precipitation in a tropical dry forest of Northwestern Mexico

Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change.

Forecasting changes in the demographic situation in the Republic of Sakha (Yakutia)

Subject. This article discusses the issues related to the population of the Republic of Sakha (Yakutia). Objectives. The article aims to predict the demographic situation in the Republic of Sakha (Yakutia) on the basis of socio-economic factors. Methods. For the study, we used a statistical analysis, time series regression modeling, and the forecasting method. Results. Based on regression modeling of time series, eleven factors influencing the change in the population of the Republic of Sakha (Yakutia) were identified. The article presents two developed forecasts of changes in the demographic situation. Conclusions. The development of industry, science, and improving the quality of life of the population contribute to the improvement of the demographic situation in the Republic of Sakha (Yakutia). The results obtained can be used to develop a comprehensive development programme for the Republic of Sakha (Yakutia).

In-Person and Teleconsultation Services at a National Hospital in Peru: Time Series Analysis of General and Psychiatric Care Amid the COVID-19 Pandemic.

The COVID-19 pandemic led to a global reduction in health care accessibility for both infected and noninfected patients, posing a particular burden on those with chronic conditions, including mental health issues. Peru experienced significant devastation from the pandemic, resulting in a collapsed health care system and leading to the world's highest per capita mortality rate as a result of COVID-19. Understanding the trends in health care utilization, particularly in mental health care, is crucial for informing pandemic response efforts and guiding future recovery strategies. This study aims to analyze the trends of outpatient medical and psychiatric consultations during the COVID-19 pandemic in a national hospital in Peru. This observational study was conducted at a national hospital in Lima, Peru. We analyzed data on user care across all services, including psychiatric services, from May 2019 to December 2022. The data were calculated for users served per month, including the number of users seen monthly in mental health services. Sociodemographic variables such as sex (female or male), age (≥0 years), type of medical appointment (regular or additional), and modality of care (in-person or teleconsultations) were taken into account. An interrupted time series regression model was conducted to assess the number of outpatient medical and psychiatric consultations. Subgroup analyses were performed based on service modality, including overall consultations, telemonitoring/teleconsultations only, or face-to-face only, for all service users and for mental health service users. A total of 1,515,439 participants were included, with females comprising 275,444/484,994 (56.80%) of the samples. Only 345,605/1,515,439 (22.81%) visits involved telemedicine. The total monthly outpatient visits were significantly reduced compared with the expected projection (P<.001) at the beginning of the pandemic, followed by a later monthly increment of 298.7 users. Face-to-face interventions experienced a significant reduction at the beginning of the pandemic (P<.001), gradually recovering in the following months. By contrast, telemedicine use initially increased but subsequently declined toward the end of the pandemic. A similar trend was observed in mental health units. During the pandemic years, health care utilization in both general and psychiatric services experienced a significant decrease, particularly at the beginning of the pandemic (March 2020). However, no significant trends were observed in either case throughout the pandemic period. Telemedicine consultations witnessed a significant increase overall during this period, particularly among mental health users.