Time Series Linear Regression Research Articles

Failed/difficult Intubation comparing between pre-COVID-19 and COVID-19 pandemic period using a national insurance claims database and information system of a university hospital

IntroductionEndotracheal intubation can be difficult or even fail under certain patient and anaesthesiologist conditions. During the COVID-19 pandemic a country-wide lockdown policy was enforced in Thailand which stipulated that anaesthesiologists wear personal protective equipment, powered air purifying respirator, or goggles and surgical/N95 mask during the intubation procedure. Thus, an anaesthesiologist’s vision is restricted and grip on the equipment less sure. Under these conditions, the incidence of difficult intubation was expected to increase.MethodsThis time-series study was based on the aggregated age- and sex-standardized monthly incidence of difficult intubation among all intubated patients whose data were recorded in the national insurance claims database and among patients recorded in the records of a university hospital from January 2018 to September 2022. Changes in incidence of difficult intubation following the implementation of a lockdown policy from 26 March 2020 during the COVID-19 pandemic were explored using negative binomial regression and interrupted linear regression time-series analysis.ResultsData of 922,274 individuals in the national database and 95,457 individuals in the university database were retrieved. The overall incidence of difficult intubation in both settings dropped by 25% following lockdown, significantly so in the national database (p < 0.001). At the point of interruption, a significant drop in level was evident in the national data (of 1.682 per thousand per month, P = 0.003) and a non-significant drop at the university level (of 1.118 per thousand per month, P = 0.304).DiscussionThe decreased incidence of difficult intubation during the lockdown period was contrary to expectation but might be related to the deployment solely of anaesthesiologists and more experienced anaesthetic staff using videolaryngoscopes during lockdown following the recommendation for intubation during respiratory disease pandemics.

Excess mortality and hospitalizations associated with seasonal influenza in patients with heart failure

Abstract Background Influenza virus may cause severe infection in patients with heart failure. It is accepted that influenza infection is associated with increased morbidity and mortality in patients with heart failure. However, less is known about the excess burden of morbidity and mortality caused by influenza infection in patients with heart failure at a population level. Purpose To estimate the excess burden of morbidity and mortality as determined by annual excess number of deaths and hospitalizations associated with influenza infection in patients with heart failure. Methods We collected nationwide data on weekly number of deaths and hospitalizations among patents with heart failure in Denmark and weekly estimates of influenza circulation as determined by the proportion of positive influenza samples analyzed at all Danish Hospitals. These data were correlated in a time series linear regression model and this model was used to estimate the annual excess number of deaths and hospitalizations attributable to influenza circulation among patients with heart failure in Denmark. Results Data were available from 2010 to 2018 encompassing 8 influenza seasons with an annual mean of 25180 samples tested for influenza. Among an annual mean of 70570 patients with heart failure, our model estimated that influenza activity was associated with an annual excess of 250 all cause deaths (95%CI 144-489) corresponding to 2.6% of all all-cause deaths (95%CI 1.5% - 5.1%). Similarly, influenza activity was associated with an annual excess of 115 cardiovascular deaths (95%CI 62-244) corresponding to 2.9% of all cardiovascular deaths (95%CI 1.5% - 6.1%) among patients with heart failure. Influenza activity was also associated with an annual excess of 251 hospitalizations for pneumonia or influenza (95%CI 107-533) corresponding to 5.0% of all hospitalizations for pneumonia or influenza. Conclusion Our results indicate that influenza activity likely causes substantial morbidity and mortality among patients with heart failure. Notably, our study suggests that approximately 2.6% of all deaths and 5.0% of all hospitalizations with influenza or pneumonia may be attributed to influenza in patients with heart failure.

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.

National Trends in the Utilization and Expenditure of Chiropractic Care in U.S. Adults: Analysis of the 2007-2016 Medical Expenditure Panel Survey

ObjectiveThis study aimed to examine the trends in the utilization and expenditure of chiropractic care in a representative sample of US adults, aged ≥18 years. MethodsSerial cross-sectional data (2007-2016) from the Medical Expenditure Panel Survey (MEPS) were examined. Weighted descriptive statistics were analyzed to obtain national estimates of chiropractic utilization and expenditure, and time-series linear regression was used to assess trends over time. Socio-demographic characteristics and musculoskeletal diagnoses associated with chiropractic use were also reported. ResultsA statistically significantly increasing trend was observed for the number of adults receiving chiropractic care (p < .05), number of visits (p < .05) and utilization rate (P < .05) from 2007 to 2016. A similar trend was not found for chiropractic expenditure during this period (P > .05). The mean number of visits was 8.3 visits per year, with a mean expenditure of $86.94 USD per visit and $721.43 USD per person per year. Mean age of adult chiropractic users each year ranged between 48.6 and 51.2 years old, and users were primarily female (56.3%-60.4%), White persons (90.1%-93.5%), and privately insured (77.3%-82.8%). The most prevalent musculoskeletal diagnoses associated with chiropractic use were low back conditions (45.4%-58.1%), inflammatory/degenerative disc or joint conditions (21.2%-26.8%) and head and neck complaints (9.8%-13.7%). ConclusionThe findings showed statistically significant increasing trends for the number of adult chiropractic users, the number of visits, and percent utilization of chiropractic care from 2007 to 2016. Conversely, we found a statistically null trend for the total annual expenditure on chiropractic care during the same time period. Findings from this analysis imply that, while chiropractic care appears to be growing, it may still be under-utilized, and more efforts should be devoted to ensuring sustained growth and a larger role in the management of musculoskeletal health.

Excess Mortality and Hospitalizations Associated With Seasonal Influenza in Patients With Heart Failure

BackgroundInfluenza virus may cause severe infection in patients with heart failure. It is known that influenza infection is associated with increased morbidity and mortality in patients with heart failure. However, less is known about the excess burden of morbidity and mortality caused by influenza infection in patients with heart failure at a population level. ObjectivesThe purpose of this study was to estimate the excess burden of morbidity and mortality as determined by annual excess number of deaths and hospitalizations associated with influenza infection in patients with heart failure in Denmark. MethodsWe collected nationwide data on weekly number of deaths and hospitalizations among patients with heart failure in Denmark and weekly estimates of influenza circulation as determined by the proportion of positive influenza samples analyzed at all Danish Hospitals. These data were correlated in a time series linear regression model, and this model was used to estimate the annual excess number of deaths and hospitalizations attributable to influenza circulation among patients with heart failure in Denmark. The model also included data on weekly mean temperature and restricted cubic spline terms to account for seasonality and trends over time. ResultsData were available from 2010 to 2018 encompassing 8 influenza seasons with an annual mean of 25,180 samples tested for influenza at Danish hospitals. Among an annual mean of 70,570 patients with heart failure, our model estimated that influenza activity was associated with an annual excess of 250 all-cause deaths (95% CI: 144-489 deaths) corresponding to 2.6% of all all-cause deaths (95% CI: 1.5%-5.1%) in patients with heart failure. Similarly, influenza activity was associated with an annual excess of 115 cardiovascular deaths (95% CI: 62-244 deaths) corresponding to 2.9% of all cardiovascular deaths (95% CI: 1.5%-6.1%). Influenza activity was also associated with an annual excess of 251 hospitalizations for pneumonia or influenza (95% CI: 107-533 hospitalizations) corresponding to 5.0% of all hospitalizations for pneumonia or influenza. ConclusionsOur results indicate that influenza activity likely causes substantial morbidity and mortality among patients with heart failure. Notably, our study suggests that approximately 2.6% of all deaths and 5.0% of all hospitalizations with influenza or pneumonia may be attributed to influenza in patients with heart failure.

Monitoring momentary subjective well-being and psychotic experiences during antipsychotic dose reduction: Two single-case time series experience sampling method pilot study.

Personalized dose optimization is desirable to improve subjective well-being and support rehabilitation. Therefore, N = 1 trials investigating tools to self-manage functional outcomes during dose optimization are necessary. The aim of this pilot study was to determine in two N = 1 trials whether an experience sampling method (ESM) smartphone app could be used to monitor and detect changes in subjective well-being and psychotic experiences during and after dose reduction. Two participants with a psychotic disorder in remission underwent gradual dose reduction under a longitudinal self-monitoring paradigm, accompanied by regular clinical monitoring by the attending physician. Subjective well-being was monitored with an (ESM) app through momentary affective experience (positive and negative affect) and physical well-being (tiredness, feeling unwell, and pain). Momentary psychotic experiences were assessed with four items. Time-series linear regression models were used to detect changes in reduction phases compared to baseline. Whereas significant improvements in subjective well-being and psychotic experiences during dose reduction were detected in one participant, the opposite was demonstrated for the other participant, showing worsened subjective well-being and increased psychotic experiences in the reduction phase compared to baseline. Self-monitoring with an ESM smartphone app offers a tool for detecting changes in subjective well-being and psychotic experiences during antipsychotic dose reduction. In this way, ESM may increase an individual's sense of control and empowerment. It may further facilitate dose optimization and shared decision-making at an individual level, which is required considering the high heterogeneity in psychotic disorders. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Inpatient Hospice Impact on Blood Culture Practices Near the Time of Death, Tertiary Center, Northern California, 2019–2023

Introduction: Many central line-associated bloodstream infections are identified in patients nearing the end of life. Stanford Health Care recently introduced the General Inpatient Hospice program. This program offers inpatient hospice care for patients who, due to uncontrolled symptoms, cannot be discharged to a hospice facility or receive home hospice care. We investigated whether this program would impact blood cultures practices near the time of death. Methods: We performed a retrospective cohort study at Stanford Health Care using records of blood culture events from May 2019 to October 2023. We defined a blood culture near-death as those collected within 2 days before the date of death. We performed an interrupted time series linear regression before and after the implementation of the General Inpatient Hospice program on July 1, 2022 to assess blood culture intensity near-death. Blood culture intensity was defined as the proportion of cultures collected near-death in relation to the total number of blood cultures. Additionally, we calculated blood culture positivity rate, which was defined as the proportion of positive blood cultures among all those collected during our study period. Results: Out of 220,269 blood cultures from 24,955 unique patients, a total of 6,147 cultures (9%) were obtained near the time of death. Among these subjects, the median age was 65 years (range 20–102), with 43% identifying as being of White race-ethnicity and 57% as male. Of these cultures, 3044 were positive (49.5%), with Escherichia coli (618, 24%), Klebsiella pneumoniae (341, 13%), and Staphylococcus aureus (166, 10%) being the most common organisms. After the implementation of the General Inpatient Hospice program, the median enrollment was 12 patients (range 3–18) and the median mortality rate was 2.3% (range 2–3%). The blood culture intensity near death decreased by 0.81%, a change that was not statistically significant (95% CI -2.4% to 0.8%, p=.32; Figure 1). Subsequently, the blood culture intensity showed a non-significant increasing trend of 0.05% (95% CI -0.1% to 0.2%, p=0.53). The blood culture positivity rate near the time of death increased by 16% following the intervention, but this increase was not statistically significant (95% CI – 11.8% to 43.3%, p=.26; Figure 2), and it was followed by a non-significant downtrend of 1.9% (95% CI -3.9% to 1.4%, p=.36). Conclusion: We found no significant association between the implementation of an inpatient hospice program and blood culture practices near the time of death, likely due to low patient enrollment.

Study of Factors Influencing U.S. Treasury Yields Based on Time Series Linear Regression Models

Studying the impact of changes in the savings rate on fluctuations in US Treasuries is significant. This paper conducts a linear regression analysis of the yield of US Treasuries, inflation rate, GDP growth rate, and US savings rate over the past decade, aiming to explore their relationships and influences. Based on economic data from the United States, a model is constructed, which is further applied to data from the European Union to validate its applicability and accuracy across different economic systems and to investigate the impact of disparities in data between different regions on the results. After analyzing the data and obtaining results, various types of economic data from the European Union are used as model variables for testing. Following a correlation analysis of the data, the conclusion is drawn that even different regions or countries exhibit varying positive or negative correlations between their economic data and US Treasury yield fluctuations. This paper delves into the analysis and comparison of the interaction between US Treasury yields and economic indicators of both the United States and the European Union, exploring whether these interactions manifest differently in the two distinct economic systems.

Dynamics of the 2021 unrest at Changbaishan Tianchi volcano from ALOS-2/PALSAR-2 and seismic data

The Changbaishan Tianchi intraplate volcano is one of the most active and hazardous volcanoes of NE Asia, characterized by a summit caldera formed after the 946 CE ‘Millennium’ Plinian eruption. From December 2020 to June 2021, the frequency and magnitude of earthquakes at Tianchi were significantly higher than during background periods, with hundreds of earthquakes (46 events per month in average) and reaching a local magnitude of ML 3.1. This study reports a comprehensive deformation analysis and geophysical inversion scheme aimed to unveil the dynamics of this period. Multi-temporal InSAR analysis results of 32 ALOS-2 images from 2018 to 2022 show that the surface deformation is a combination of seasonal fluctuations (± 25 mm in average, with a maximum ± 45 mm) and a long-term positive component. The least squares linear regression of the deformation time series and temperature data, isolates the seasonal fluctuations, revealing a clear uplift-subsidence process from June 2020 to July 2021 in the caldera area. To constrain the Tianchi plumbing system dynamics, a combined inversion scheme consisting of three deformation sources is designed. The inversion results and the seismic records indicate that Tianchi volcano has experienced a low-level unrest episode from December 2020 to June 2021. The shallower plumbing system, located at about 5–9 km depth and modelled by pressurized spheroids, underwent a cumulative volume increase of 26 × 106 m3 from November 2018 to April 2021, followed by a volume decrease of 9 × 106 m3 from April to July 2021. This suggests magma uprising from the 14 km deep storage zone to the shallower plumbing system, followed by depressurization of the plumbing system due to the escape of fluids. This research provides a comprehensive understanding of the magma and fluid migration dynamics within the Tianchi multi-level plumbing system for the first time.

Economic Growth Model with Regional Expenditure as a Moderation Variable: Scale in Berau Regency

The motivation of this paper aims to investigate the relationship between local taxes, DAU, and DBH on the economic growth of Berau Regency. Apart from that, the paper also identifies the relationship between regional taxes, DAU, and DBH on economic growth which interacts with regional expenditure in Berau Regency. Government revenue is an important instrument for regions to foster economic growth. At the same time, government spending is used as a fiscal support to review the degree of budget absorption. With surplus revenue performance, it will generate financing which also plays a vital function in boosting economic growth. Secondary data for 2013–2022 was collected from government budget documents. Then, the data is tabulated using a time-series linear regression model with moderating variables. The statistical output finds that regional taxes and DBH have a negative effect on economic growth. However, DAU actually has a positive effect on economic growth. Uniquely, the interaction between regional taxes and DAU which is moderated by regional spending has a positive influence on economic growth. Then, regional spending plays a negative role in the relationship between DBH and economic growth. Future implications of considering practical regulations that do not only depend on income from the DAU side, but must focus on the existence of regional taxes in Berau Regency. By optimizing regional taxes, it also allows for a large fiscal gap in terms of regional spending. Financing the development of Berau Regency requires adequate expenditure allocation.

Fire, Rain and CO2: Potential Drivers of Tropical Savanna Vegetation Change, with Implications for Carbon Crediting

A global trend of increasing tree cover in savannas has been observed and ascribed to a range of possible causes, including CO2 levels, changing rainfall and fire frequency. We tested these explanations in the Australian tropical savanna, taking 96 savanna ‘cool burning’ projects from Australia’s emissions offset scheme as case studies. We obtained readings of tree cover and explanatory variables from published remote sensing or spatial data sources. These were analysed using time-series linear regression to obtain coefficients for the influence of severe fire occurrence, annual rainfall and prior percentage tree cover. Although statistically significant coefficients for the key variables were found in only half (severe fire) or one quarter (rainfall) of the individual project models, when comparing all the model coefficients across the rainfall gradient, ecologically coherent explanations emerge. No residual trend was observed, suggesting rising CO2 levels have not influenced tree cover over the study period. Our approach models tree cover change by separating ecological drivers from human-controlled factors such as fire management. This is an essential design feature of national emissions inventories and emissions offsets programs, where crediting must be additional to the expected baseline, and arise from human activity.

Respiratory Syncytial Virus-Associated Hospital Admissions by Deprivation Levels Among Children and Adults in Scotland.

Socioeconomic deprivation may predispose individuals to respiratory tract infections. We estimated RSV-associated hospitalizations by socioeconomic deprivation in Scotland. Using national routine health care records and virological surveillance from 2010 to 2016, we used a time-series linear regression model and a direct measurement based on ICD-10 coded diagnoses to estimate RSV-associated hospitalizations by Scottish Index of Multiple Deprivation (SIMD) quintile and age in comparison to influenza-associated hospitalizations. We estimated an annual average rate per 1000 people of 0.76 (95% CI: 0.43-0.90) in the least deprived group to 1.51 (1.03-1.79) for the most deprived group using model-based approach. The rate ratio (RR) was 1.96 (1.23-3.25), 1.60 (1.0-2.66), 1.35 (0.85-2.25), and 1.12 (0.7-1.85) in the 1st to 4th quintile versus the least deprived group. The pattern of RSV-associated hospitalization rates variation with SIMD was most pronounced in children 0-2y. The ICD-10 approach provided much lower rates than the model-based approach but yielded similar RR estimates between SIMD. Influenza-associated hospitalization rate generally increased with higher deprivation levels among individuals 1y+. Higher RSV and influenza hospitalization rates are related to higher deprivation levels. Differences between deprivation levels are most pronounced in infants and young children for RSV, and are more apparent among individuals 1y+ for influenza.

Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration

As electric vehicles (EVs) continue to grow in popularity, the demand for efficient and effective EV charging infrastructure is rapidly increasing. However, the design and deployment of such infrastructure faces numerous challenges, some of which include demand uncertainty, EV network layout, maintenance cost, and quality of service (QoS), and have proven to be particularly challenging in cases where all objectives need to be addressed simultaneously. To address these challenges, this study proposes a hierarchical iterative optimization approach that aims to minimize the demand–supply mismatch at a higher level and considers both cost and QoS under uncertainty at a lower level in a dynamic EV charging network. Specifically, the proposed approach uses a time-series linear regression ensemble to accurately predict future demand at demand points, which is then used to match demand at demand points with supply at supply points. We then model the infrastructure allocation problem as a knapsack problem and use an allocation algorithm to determine the optimal number of charging stations at each supply point. The infrastructure is allocated to minimize total cost and maximize QoS. A framework for EV infrastructure optimization is designed and can be applied to any type of network. The effectiveness of our proposed approach is demonstrated through extensive experimentation on various datasets by comparing with existing state-of-the-art approaches. Simulation results show that the proposed approach is robust to different EV networks, handles uncertainty well, can effectively address the challenges associated with EV infrastructure optimization, and can provide policy makers and infrastructure planners with a practical and efficient tool for designing and deploying charging infrastructure. This study contributes to the field of EV infrastructure network optimization by modeling QoS and proposing a hierarchical optimization framework that effectively addresses the challenges associated with the design and deployment of charging infrastructure.

Dental Pain Medication Prescriptions in Minas Gerais, Brazil (2011-2021): A Time-Series Analysis.

To describe trends of dentist-prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and analgesics, from January 2011 to December 2021, as well as to examine the relationship between these trends and characteristics of public oral health services in Minas Gerais, Brazil. In this time-series analysis, all drugs were classified according to the Anatomical Therapeutic Chemical classification system. Drugs categorized as NSAIDs (M01A), and other analgesics and antipyretics (N02B) were included for analysis. The outcome was the number of Defined Daily Doses (DDDs)/1000 inhabitants/year for NSAIDs and analgesics in each town. Covariates referred to characteristics of public oral health services, such as coverage, estimates of dental procedures, and frequency of toothache. Linear time-series regression models were used to determine the influence of covariates on the outcome. Overall, there were 58,482 prescriptions of NSAIDs recorded in thirty-eight towns, while 47,499 prescriptions of analgesics in forty-three towns. For each year, there was a 0.38 (p < 0.001), and 0.28 (p < 0.001) increase in the average log of DDD/1000 inhabitants/year for NSAIDs and analgesics, respectively. A positive association was detected between toothache (p < 0.001) and the prescription of NSAIDs. Over the eleven years, there was a general rising trend in the prescriptions. Toothache was the only characteristic of public oral health services associated with the prescription rates of NSAIDs, implying that as the frequency of toothaches increase, so do the prescriptions of NSAIDs in the studied towns.

Increases in New but Not Returning Families to a Midwestern Food Pantry Network During the COVID-19 Pandemic

BackgroundFood pantries are a resource for those experiencing food insecurity. ObjectiveThe purpose of this study was to evaluate changes in food pantry utilization and volunteerism for a food pantry network during the COVID-19 pandemic. DesignThis 36-month longitudinal, observational study is a secondary analysis of data collected from an Ohio food pantry network. Participants/settingParticipants were clients of a food pantry network. The data represent visits from 12 months prepandemic (March 2019 through February 2020), during the pandemic (March 2020 through February 2021), and after vaccines were readily available (March 2021 through February 2022). Main outcome measuresEach data point represents a visit to the pantry network. The main outcome measures were total, returning, and new visits (ie, households that had not previously used this pantry network). The secondary outcome was volunteer hours by month. Statistical analysisIn order to account for month-to-month variability in pantry utilization, the data were analyzed using a time-series linear regression analysis with the month as the unit of analysis. ResultsA total of 174,397 visits were recorded during the course of 36 months. Sixty-nine percent of reporting household members were female, 48% reported at least 1 senior in the home, and 41% reported at least 1 child at home. There was no significant change in total or returning visits during the pandemic or after vaccines were available compared with prepandemic levels. However, there was a significant increase in new households to the food pantry network during the pandemic compared with prepandemic (P = .05). Volunteer hours decreased significantly during the pandemic compared with prepandemic months and remained low even after vaccines were available (P = .004 and P = .003, respectively). ConclusionsAlthough there was an increase in households new to the food pantry, overall utilization did not increase.

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

This article presents sales trends and market basket analysis for Food and Beverages (F&amp;B) items recommendation using linear regression time series and association rules. The purpose of this study is to help businesses to make better informed decisions by understanding their customers’ purchasing behaviors and the sales trends of the F&amp;B items. This includes the study of both cross- selling and up-selling patterns using market basket analysis. To improve the market basket analysis, food name clustering has been carried out to group those foods having similarity ratio higher or equal to 80 based on the Levenshtein distances between the food names. This study analyzes sales data from a mobile app platform that register the Malaysian F&amp;B hawkers (or merchants) and app users (or buyers) who sell and purchase F&amp;B respectively. This study is based on sales trends analysis and association rules of product recommendations by examining FP-Growth and the relationship between product items bought. This study provides valuable insights for local Malaysian hawkers who are looking to improve their F&amp;B items recommendations and to make better data-driven decisions while planning their marketing activities. It helps to improve sales by either up or cross-sell their products more effectively.

A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators.

This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.

Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.

Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture. In Japan, the de facto population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments. Combined with this data, we conducted a time series linear regression analysis to explore the relationship between daily reported case counts of COVID-19 in Tokyo, Osaka, and Aichi, and night-time de facto population in downtown areas estimated from mobile phone location data, from February 2020 to May 2022. As an approximation of the effective reproduction number, the weekly ratio of cases was used. Models using night-time population with lags ranging from 7 to 14 days were tested. In time-varying regression analysis, the night-time population level and the daily change in night-time population level were included as explanatory variables. In the fixed-effect regression analysis, the inclusion of either the night-time population level or daily change, or both, as explanatory variables was tested, and autocorrelation was adjusted by introducing first-order autoregressive error of residuals. In both regression analyses, the lag of night-time population used in best fit models was determined using the information criterion. In the time-varying regression analysis, night-time population level tended to show positive to neutral effects on COVID-19 transmission, whereas the daily change of night-time population showed neutral to negative effects. The fixed-effect regression analysis revealed that for Tokyo and Osaka, regression models with 8-day-lagged night-time population level and daily change were the best fit, whereas in Aichi, the model using only the 9-day-lagged night-time population level was the best fit using the widely applicable information criterion. For all regions, the best-fit model suggested a positive relationship between night-time population and transmissibility, which was maintained over time. Our results revealed that, regardless of the period of interest, a positive relationship between night-time population levels and COVID-19 dynamics was observed. The introduction of vaccinations and major outbreaks of Omicron BA. Two subvariants in Japan did not dramatically change the relationship between night-time population and COVID-19 dynamics in three megacities in Japan. Monitoring the night-time population continues to be crucial for understanding and forecasting the short-term future of COVID-19 incidence.