Time Series Intervention Analysis Research Articles

Long-term patterns of post-fire harvest diverge among ownerships in the Pacific West, U.S.A.

Abstract Post-fire harvest (PFH) is a forest management practice designed to salvage value from burned timber, mitigate safety hazards from dead trees, reduce long-term fuels, and prepare sites for replanting. Despite public controversy and extensive ecological research, little is known about how much PFH occurs on private and public lands in the U.S. Pacific West, or how practices changed with shifting forest policy and increasing area burned over the last three decades. We mapped PFH across 2.2M burned hectares in California, Oregon, and Washington between 1986-2017 and used time series intervention analysis to compare trends in area, rate (% of burned area harvested), and mean patch size between private (0.5M ha) and federal (1.6M ha) forest land and across a gradient of burn severity. Harvest rates varied by ownership (4.9% federal, 18.6% private, 8.0% overall), and practices evolved and diverged over the study period. PFH area and rate declined across all ownerships in the mid-1990s during a period of reduced fire activity. As area burned increased between the early 2000s and late 2010s, PFH area rebounded and surpassed late-1980s levels, while rates remained relatively low. On federal lands, PFH practices shifted in the early-to-mid 1990s towards lower rates (10.3% to 3.8%) and smaller patches (6.0 to 3.3 ha), following policy changes and increased litigation. PFH rates on federal lands decreased at all levels of burn severity, with the largest decreases (6.2% to 1.2%) in forests with low tree mortality (i.e. fire refugia). Conversely, private PFH rates and mean patch sizes more than doubled in forests burned at very low-to-moderate severity. Our results highlight how PFH practices have shifted with policy, socio-economic pressure, and increasing area burned over 31 years in the Pacific West. A similar area of post-fire harvest is now dispersed over larger fires, with practices diverging substantially between ownerships.

Assessing temporary product-specific subsidies: a time series intervention analysis

Assessing temporary product-specific subsidies: a time series intervention analysis

Impact of Xpert MTB/RIF implementation in tuberculosis case detection and control in Brazil: a nationwide intervention time-series analysis (2011–2022)

Since 2014, Brazil has gradually implemented the Xpert MTB/RIF (Xpert) test to enhance early tuberculosis (TB) and drug-resistant (DR-TB) detection and control, yet its nationwide impact remains underexplored. Our study conducts an intervention time-series analysis (ITSA) to evaluate how the Xpert's implementation has improved TB and DR-TB detection nationwide. 1,061,776 cases from Brazil's National TB Registry (2011-2022) were reviewed and ITSA (2011-2019) was used to gauge the impact of the Xpert's adoption on TB and DR-TB notification. Granger Causality and dynamic regression modelling determined if incorporating Xpert testing as an external regressor enhanced forecasting accuracy for Brazil's future TB trends. Xpert implementation resulted in a 9.7% increase in TB notification and substantial improvements in DR-TB (63.6%) and drug-susceptible TB (92.1%) detection compared to expected notifications if it had not been implemented. Xpert testing counts also presented a time-dependent relationship with DR-TB detection post-implementation, and improved predictions in forecasting models, which depicted a potential increase in TB and DR-TB detection in the next six years. This study underscores the critical role of Xpert's adoption in boosting TB and DR-TB detection in Brazil, reinforcing the case for its widespread use in disease control. Improvements in prediction accuracy resulting from integrating Xpert data are crucial for allocating resources and reducing the incidence of TB. By acknowledging Xpert's role in both disease control and improving predictions, we advocate for its expanded use and further research into advanced molecular diagnostics for effective TB and DR-TB control. FIOCRUZ.

Antimicrobial consumption in an acute NHS Trust during the COVID-19 pandemic: intervention time series analysis.

To determine the impact of the COVID-19 pandemic on antimicrobial consumption and trends of therapeutic drugs for COVID-19 treatments, including corticosteroids, remdesivir and monoclonal antibodies (tocilizumab) from April 2017 to September 2022 in a secondary care NHS Trust in England. A retrospective intervention time series analysis was conducted for April 2017 to September 2022 at the Mid Yorkshire Teaching NHS Trust. Data were retrieved from the pharmacy dispensing system as defined daily doses (DDDs) monthly and reported per 1000 occupied bed days (OBDs). Antimicrobial consumption and COVID-19 treatment options were measured. DDDs were calculated according to the classification of antimicrobials for systemic use (J01) and for other drugs classification. Trends for antimicrobial consumption and other therapeutic drugs for treating COVID-19 were also determined in each wave in England. During the pandemic: total antibiotic consumption decreased from 826.4 to 728.2 DDDs per 1000 OBDs (P = 0.0067); piperacillin/tazobactam use increased (P < 0.0001) and ciprofloxacin use decreased (P < 0.0001); there were no changes in Access, Watch, Reserve antibiotic use, and the proportion of antifungal consumption was consistent throughout the study. The use of total antibiotics (P = 0.024), levofloxacin (P = 0.0007), piperacillin/tazobactam (P = 0.0015) and co-amoxiclav (P = 0.0198) increased during wave one. Consumption of COVID-19 treatment drugs was highest during wave two, with 624.3 DDDs per 1000 OBDs for dexamethasone (P = 0.4441), 6.8 DDDs per 1000 OBDs for remdesivir (P < 0.0001) and 35.01 DDDs per 1000 OBDs for tocilizumab (P = 0.2544). This study determined the consumption of antimicrobials trends before and during the pandemic. The individual wave antimicrobial consumption indicates maximum consumption in the first wave, advocating for antimicrobial stewardship and preparedness for future pandemics.

Latent Autoregressive Student-t Prior Process Models to Assess Impact of Interventions in Time Series

With the advent of the “Internet of Things” (IoT), insurers are increasingly leveraging remote sensor technology in the development of novel insurance products and risk management programs. For example, Hartford Steam Boiler’s (HSB) IoT freeze loss program uses IoT temperature sensors to monitor indoor temperatures in locations at high risk of water-pipe burst (freeze loss) with the goal of reducing insurances losses via real-time monitoring of the temperature data streams. In the event these monitoring systems detect a potentially risky temperature environment, an alert is sent to the end-insured (business manager, tenant, maintenance staff, etc.), prompting them to take remedial action by raising temperatures. In the event that an alert is sent and freeze loss occurs, the firm is not liable for any damages incurred by the event. For the program to be effective, there must be a reliable method of verifying if customers took appropriate corrective action after receiving an alert. Due to the program’s scale, direct follow up via text or phone calls is not possible for every alert event. In addition, direct feedback from customers is not necessarily reliable. In this paper, we propose the use of a non-linear, auto-regressive time series model, coupled with the time series intervention analysis method known as causal impact, to directly evaluate whether or not a customer took action directly from IoT temperature streams. Our method offers several distinct advantages over other methods as it is (a) readily scalable with continued program growth, (b) entirely automated, and (c) inherently less biased than human labelers or direct customer response. We demonstrate the efficacy of our method using a sample of actual freeze alert events from the freeze loss program.

The short-term impact of terrorism on public mental health: an emergency primary care approach

BackgroundTerrorist attacks commonly have mental health consequences for those directly affected. Existing research is, however, divided when it comes to how and whether terrorist attacks affect the general population’s mental health. There is a need for studies investigating a broader range of mental health reactions to understand more about how different groups of the population are affected by terrorist attacks, while also illuminating important systemic factors.MethodsIn this study we investigated whether there was any change in the number of consultations with out-of-hours emergency primary care for psychological reactions in association with the 2011 terrorist attacks in Norway. Data covering the entire Norwegian population’s primary care contacts in 2008–2013, where the reason for encounter was coded as psychological concerns or psychiatric disorders, were studied. A time series intervention analysis, using ARIMA modelling, was used to estimate whether there was indeed a change in healthcare utilisation associated with the terrorist attacks.ResultsThe analysis uncovered an increase in contacts with emergency primary care by the overall population for mental health concerns associated with the terrorist attacks. When divided into groups according to geographical proximity to attacks, no significant change was found in the area closest to the attack in Oslo, whereas an increase was found for the rest of the country. There was also heterogeneity across different age groups. An increase was found among youths, young adults, and middle-aged people, but not the other age groups, and an increase was found for both men and women.ConclusionsThese findings highlight the need for primary care services to be prepared to meet mental health reactions in the general population when planning for healthcare provision in the aftermath of terrorism. Simultaneously, it should be noted that needs may vary across different groups of the population.

시계열 분석을 통한 프로야구리그 경기력 예측

시계열 분석을 통한 프로야구리그 경기력 예측

시계열 분석을 통한 프로야구리그 경기력 예측

시계열 분석을 통한 프로야구리그 경기력 예측

Using intervention analysis to evaluate the trends in release rates of recreational fisheries following extensive management changes

Changes to management of a fisheries resource are often required to ensure ongoing sustainability. However, such changes can sometimes lead to unintended effects such as increased release rates and associated post-release mortality. These effects may be highly variable between species and areas. Recreational fishing management changes were introduced on the west coast of Australia in 2009/10 to recover stocks of demersal scalefish. Key changes included reducing mixed species bag limits across management zones and increasing the minimum size limit for one species in some management zones. The restrictive catch limits resulted in increased release rates of key demersal species. However, whether such increases are significant and sustained over time, and thus of management concern, have not been evaluated. We carried out intervention time series analysis to evaluate the impact of management changes on release rates of four key demersal species for the recreational sector in metropolitan and regional management zones covering ∼8° latitude using an 18-year time series of charter recreational fishery data from July 2002 to January 2020. We observed varying responses in release rates by species and zones, the most common of which were a step increase, a ramp and a temporary increase that decayed. These responses may be related to targeted management changes which influenced fisher behaviour, perceived recreational value of some species and recruitment variation. Our study demonstrates that intervention analysis, which has seen limited use in this context, can assist in evaluating the impact of management changes on different species for recreational fisheries.

Safety evaluation of centerline rumble strips on rural two-lane undivided highways: Application of intervention time series analysis

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

The impact of the COVID-19 pandemic on antibiotic consumption and prevalence of pathogens in primary and secondary healthcare settings in Northern Ireland.

To evaluate the impact of the COVID-19 pandemic on the patterns of antimicrobial use and the incidence of pathogens in primary and secondary healthcare settings in Northern Ireland. Data were collected on antibiotic use and Gram-positive and Gram-negative pathogens from primary and secondary healthcare settings in Northern Ireland for the period before (January 2015-March 2020) and during (April 2020-December 2021) the pandemic. Time series intervention analysis methods were utilized. In the hospital setting, the mean total hospital antibiotic consumption during the pandemic was 1864.5 defined daily doses (DDDs) per 1000 occupied-bed days (OBD), showing no significant change from pre-pandemic (P = .7365). During the pandemic, the use of second-generation cephalosporins, third-generation cephalosporins, co-amoxiclav and levofloxacin increased, there was a decrease in the percentage use of the hospital Access group (P = .0083) and an increase in the percentage use of Watch group (P = .0040), and the number of hospital Klebsiella oxytoca and methicillin-susceptible Staphylococcus aureus cases increased. In primary care, the mean total antibiotic consumption during the COVID-19 pandemic was 20.53 DDDs per 1000 inhabitants per day (DID), compared to 25.56 DID before the COVID-19 pandemic (P = .0071). During the pandemic, there was a decrease in the use of several antibiotic classes, an increase in the percentage use of the Reserve group (P = .0032) and an increase in the number of community-onset Pseudomonas aeruginosa cases. This study provides details of both changes in antibiotic consumption and the prevalence of infections in hospitals and primary care before and during the COVID-19 pandemic that emphasize the importance of antimicrobial stewardship in pandemic situations.

Intervention Time Series Analysis and Forecasting of Organ Donor Transplants in the US during the COVID-19 Era

The COVID-19 pandemic has had a catastrophic effect on the healthcare system including organ transplants worldwide. The number of living donor transplants performed in the US was affected more significantly by the pandemic with a 22.6% decrease in counts from 2019 to 2020 due to concerns of unnecessarily exposing potential living donors and living donor recipients to possible COVID-19 infection. This paper examines donor transplant counts obtained from the United Network for Organ Sharing from January 2002 to August 2021 using an intervention time series model with March 2020 as the intervention event. Specifically, donor transplant counts are analyzed across the different organs, donor types, and some major individual sociocultural factors, which are potential conditions contributing to disparities in achieving donor transplant equity such as age, ethnicity, and gender. In addition, the kidney allocation policy implemented in March 2021 is introduced as a second intervention event for kidney donor transplants. Overall, forecasts generated by our methods are more accurate than those using seasonal autoregressive integrated moving average models without interventions and seasonal naive methods. The intervention time series model provides a forecast accuracy comparable to the exponential smoothing method.

International trends in prescription opioid sales among developed and developing economies, and the impact of the COVID-19 pandemic: A cross-sectional analysis of 66 countries.

PurposeWe sought to compare trends in opioid purchasing between developed and developing economies to understand patterns of opioid consumption, and how they were impacted by the COVID‐19 pandemic.MethodsWe conducted a retrospective cross‐sectional study of retail pharmacy opioid sales from 66 jurisdictions between July 2014 and August 2020. We measured monthly population‐adjusted rate of opioid units purchased, stratified by development group and country, and used interventional time series analysis to assess the impact of the COVID‐19 pandemic on rates of opioid purchasing among developed and developing economies separately.ResultsRates of opioid purchasing were generally higher among developed economies, although trends differed considerably by development group. Rates of opioid purchasing declined 23.8% (95% confidence interval [CI] −34.7% to 3.6%) in the 5 years prior to the pandemic in developed economies, but rose 15.2% (95% CI 4.6%–35.6%) among developing economies. In March 2020 there was a short‐term increase in the rate of opioid purchases in both developing (10.9 units/1000 population increase; p < 0.0001) and developed (145.5 units/1000 population; p < 0.0001) economies, which was followed immediately by reduced opioid purchasing of a similar scale in April–May 2020 (−14.8 and −171.8 units/1000 population in developing and developed economies, respectively; p < 0.0001).ConclusionThe COVID‐19 pandemic led to disruptions in opioid purchasing around the world; although the specific impacts varied both between and among developed and developing economies. With global variation in opioid use, there is a need to monitor these trajectories to ensure the safety of opioid use, and adequate access to pain management globally.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis, applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior, stands out as a novel and viable research direction for bridge state assessment. However, outliers inevitably exist in the monitoring data due to various interventions, which reduce the precision of model fitting and affect the forecasting results. Therefore, the identification of outliers is crucial for the accurate interpretation of the monitoring data. In this study, a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory, and the forecasting of the structural responses is carried out. There are three techniques that we focus on: (1) the modeling of seasonal autoregressive integrated moving average (SARIMA) model; (2) the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown; (3) forecasting of the model with outlier effects. The method was tested with a case study using monitoring data on a real bridge. The establishment of the original SARIMA model without considering outliers is first discussed, including the stationarity, order determination, parameter estimation and diagnostic checking of the model. Then the time-by-time iterative procedure for outlier detection, which is implemented by appropriate test statistics of the residuals, is performed. The SARIMA-outlier model is subsequently built. Finally, a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out. The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data. When the influence of outliers is taken into account, the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis.

Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis. Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)'s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed. The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001). The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

A five-component infection control bundle to permanently eliminate a carbapenem-resistant Acinetobacter baumannii spreading in an intensive care unit

BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) infection outbreaks are difficult to control and sometimes require cohorting of CRAB-positive patients or temporary ward closure for environmental cleaning. We aimed at controlling the deadly 2018 CRAB outbreak in a 12 bed- intensive care unit (ICU) including 9 beds in a 220 m2 open space. We implemented a new multimodal approach without ward closure, cohorting or temporarily limiting admissions.MethodsA five-component bundle was introduced in 2018 including reinforcement of hand hygiene and sample extension of screening, application of contact precautions to all patients, enhanced environmental sampling and the one-time application of a cycling radical environmental cleaning and disinfection procedure of the entire ICU.The ICU-CRAB incidence density (ID), ICU alcohol-based hand rub consumption and antibiotic use were calculated over a period of 6 years and intervention time series analysis was performed. Whole genome sequencing analysis (WGS) was done on clinical and environmental isolates in the study period.ResultsFrom January 2013, nosocomial ICU-CRAB ID decreased from 30.4 CRAB cases per 1000 patients-days to zero cases per 1000 patients-days. Our intervention showed a significant impact (-2.9 nosocomial ICU-CRAB cases per 1000 bed-days), while no influence was observed for antibiotic and alcohol-based hand rub (AHR) consumption.WGS demonstrated that CRAB strains were clonally related to an environmental reservoir which confirms the primary role of the environment in CRAB ICU spreading.ConclusionA five-component bundle of continuous hand hygiene improvement, extended sampling at screening including the environment, universal contact precautions and a novel cycling radical environmental cleaning and disinfection procedure proved to be effective for permanently eliminating CRAB spreading within the ICU. Cohorting, admission restriction or ICU closure were avoided.

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

Infant mortality is an important marker of the overall society health. The 3rd goal of the Sustainable Development Goals aims at reducing infant deaths that occur due to preventable causes by 2030. Due to increased infant mortality the Kenyan government introduced Free Maternal Health Care as an intervention towards reducing infant mortality through elimination of the cost burden of accessing medical care by the mother and the infant. The study examines the impact of Free Maternal Health Care on infant mortality using Intervention time series analysis particularly the intervention Box Jenkins ARIMA (Autoregressive Integrated Moving Average) model. There was significant support that Free Maternal Health Care had a significant impact on infant mortality which was estimated to be a decrease of 10.15% in infant deaths per month.

The Effects of Malaria Control Interventions on Out-patient and In-patient Malaria Cases in the Northern Region of Ghana

In this study, change detection in Out-patient and In-patient malaria cases in the Northern Region of Ghana was examined using time series intervention analysis. Data on monthly Out-patient and In-patient malaria cases obtained from the Northern Regional Health Directorate were modelled using Seasonal Autoregressive Integrated Moving Average with an Independent variable (SARIMAX) and Autoregressive Integrated Moving Average with an Independent variable (ARIMAX) models. The results revealed that SARIMAX (1, 1, 1)(1, 1, 1)12 was the best model for predicting Out-patient malaria cases while SARIMAX (1, 1, 1)(2, 1, 1)12 emerged as the best model for predicting the In-patient cases in the region. Diagnostic checks of the two models with the Ljung-Box test and Autoregressive Conditional Heteroscedasticity Lagrange Multiplier (ARCH-LM) test revealed that both models were free from higher-order serial correlation and conditional heteroscedasticity respectively. A chi-square goodness-of-fit test also revealed that there was no significant difference between the predicted values from the models and the observed values for the year 2018. The study further revealed that the coefficients of the intervention variable for the Out-patient and In-patient cases were both negative, which suggest that the intervention policy the government of Ghana implemented brought about a decline in the number of Out-patient and In-patient cases in the region.

Statistical Modeling of the Early-Stage Impact of a New Traffic Policy in Milan, Italy.

Most urban areas of the Po basin in the North of Italy are persistently affected by poor air quality and difficulty in disposing of airborne pollutants. In this context, the municipality of Milan started a multi-year progressive policy based on an extended limited traffic zone (Area B). Starting on 25 February 2019, the first phase partially restricted the circulation of some classes of highly polluting vehicles on the territory, in particular, Euro 0 petrol vehicles and Euro 0 to 3 diesel vehicles, excluding public transport. This is the early-stage of a long term policy that will restrict access to an increasing number of vehicles. The goal of this paper is to evaluate the early-stage impact of this policy on two specific vehicle-generated pollutants: total nitrogen oxides (NO) and nitrogen dioxide (NO), which are gathered by Lombardy Regional Agency for Environmental Protection (ARPA Lombardia). We use a statistical model for time series intervention analysis based on unobservable components. We use data from 2014 to 2018 for pre-policy model selection and the relatively short period up to September 2019 for early-stage policy assessment. We include weather conditions, socio-economic factors, and a counter-factual, given by the concentration of the same pollutant in other important neighbouring cities. Although the average concentrations reduced after the policy introduction, this paper argues that this could be due to other factors. Considering that the short time window may be not long enough for social adaptation to the new rules, our model does not provide statistical evidence of a positive policy effect for NO and NO. Instead, in one of the most central monitoring stations, a significant negative impact is found.

Effect of tobacco control policies on the Swedish smoking quitline using intervention time-series analysis

ObjectivesTo coherently examine the responsiveness of the Swedish National Tobacco Quitline (SNTQ) to different types of anti-smoking policies over an extended period of calendar time.DesignQuasi-experimental design with an intervention time-series...