Bayesian Structural Time Series Model Research Articles

Clinical Decision Support to Increase Emergency Department Naloxone Coprescribing: Implementation Report.

Coprescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention (CDC) best practice to mitigate the risk of fatal opioid overdose, yet coprescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported. This study aimed to rigorously evaluate and quantify the impact of CDS designed to improve emergency department (ED) naloxone coprescribing. We hypothesized CDS would increase naloxone coprescribing and the number of naloxone prescriptions filled by patients discharged from EDs in a large health care system. Following user-centered design principles, we designed and implemented a fully automated, interruptive, electronic health record-based CDS to nudge clinicians to coprescribe naloxone with high-risk opioid prescriptions. "High-risk" opioid prescriptions were defined as any opioid analgesic prescription ≥90 total morphine milligram equivalents per day or for patients with a prior diagnosis of opioid use disorder or opioid overdose. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to evaluate pragmatic CDS outcomes of reach, effectiveness, adoption, implementation, and maintenance. Effectiveness was the primary outcome of interest and was assessed by (1) constructing a Bayesian structural time-series model of the number of ED visits with naloxone coprescriptions before and after CDS implementation and (2) calculating the percentage of naloxone prescriptions associated with CDS that were filled at an outpatient pharmacy. Mann-Kendall tests were used to evaluate longitudinal trends in CDS adoption. All outcomes were analyzed in R (version 4.2.2; R Core Team). Between November 2019 and July 2023, there were 1,994,994 ED visits. CDS reached clinicians in 0.83% (16,566/1,994,994) of all visits and 15.99% (16,566/103,606) of ED visits where an opioid was prescribed at discharge. Clinicians adopted CDS, coprescribing naloxone in 34.36% (6613/19,246) of alerts. CDS was effective, increasing naloxone coprescribing from baseline by 18.1 (95% CI 17.9-18.3) coprescriptions per week or 2,327% (95% CI 3390-3490). Patients filled 43.80% (1989/4541) of naloxone coprescriptions. The CDS was implemented simultaneously at every ED and no adaptations were made to CDS postimplementation. CDS was maintained beyond the study period and maintained its effect, with adoption increasing over time (τ=0.454; P<.001). Our findings advance the evidence that electronic health record-based CDS increases the number of naloxone coprescriptions and improves the distribution of naloxone. Our time series analysis controls for secular trends and strongly suggests that minimally interruptive CDS significantly improves process outcomes.

Gender-Specific Mental Health Outcomes in Central America: A Natural Experiment

The COVID-19 pandemic and subsequent restrictions have had profound impacts on mental health worldwide, with varying effects across different demographics and regions. Specifically, COVID lockdown measures are known to have had a disparate impact on women. This study aims to better understand this phenomenon by investigating the effect of COVID-19 stringency measures on depression rates among men and women in the Republic of Nicaragua and the Republic of Honduras. The two neighboring countries serve as a natural experiment: the former noted for its relaxed approach to the pandemic, and the latter implementing stricter lockdown measures. Using a Bayesian structural time series model, yearly depression rates were analyzed in both countries, utilizing various weather indicators as predictors, including yearly rainfall and average ground temperature data. In both countries, rates of depression among women were historically higher than among men. The difference in depression rates between women and men increased during the intervention period in both countries (p &amp;lt; 0.001). However, the absolute effect of the intervention in Honduras was significantly higher (p &amp;lt; 0.001) at 0.39 (95% CI: 0.37, 0.41) compared to Nicaragua, which was 0.26 (95% CI: 0.21, 0.31). These findings suggest that the higher stringency measures in Honduras, including prolonged lockdowns and restrictions on movement, may have disproportionately affected women&amp;apos;s mental health. These results highlight the importance of considering women’s wellbeing when designing and implementing public health policies, particularly during crises like the COVID-19 pandemic.

From 30- to 5-minute settlement rule in the NEM: An early evaluation

Australia’s National Electricity Market has shifted from 30- to 5-minute settlement (5MS)— a major regulatory reform since its inception. Using a Bayesian structural time-series model over the period 1 October 2021 to 31 May 2022, we demonstrate that despite having no immediate impact on price dynamics, 5MS eventually exerts upward pressure on spot prices over time. Price volatility remains essentially unchanged. Moreover, dispatch-weighted prices (DWPs) for flexible battery generators increase. DWPs for aggregated gas generators barely change, while DWPs for incumbent black coal-fired generators unexpectedly increase. These findings support the notion that the market is gradually adapting to 5MS. For a more effective adaptation, energy policies must be in place to lower entry barriers for flexible, dispatchable generation capacity and reduce market concentrations.

Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China.

China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny. Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation. This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time-series model

This study aims to explore the application value of the Bayesian Time Structure Sequence (BSTS) model in estimating the acute hemorrhagic conjunctivitis (AHC) epidemics. The reported AHC cases spanning from January 2011 to October 2022 in China were collated. Utilizing R software, the BSTS and Autoregressive Integrated Moving Average (ARIMA) models were constructed using the data from January 2011 to December 2021. The prediction effect of both models was compared using the data from January to October 2022, and finally the AHC incidence from November 2022 to December 2023 was predicted. The results indicated that forecast errors under the BSTS model were lower than those under the ARIMA model. The actual AHC incidence in July 2022 from the ARIMA model deviated from the 95% confidence interval (CI) of the predicted value. However, the observed AHC incidence from the BSTS model fell within the 95% CI of the predicted value. Notably, the BSTS model predicted 26,474 new AHC cases in China from November 2022 to December 2023, exhibiting better prediction performance compared to the ARIMA model. This indicates that the BSTS model possesses a high application value for forecasting the epidemic trends of AHC, making it a valuable tool for disease surveillance and prevention strategies.

Impact of the COVID-19 pandemic on antidepressant consumption in the Central region of Portugal: interrupted time series.

To evaluate the impact of the pandemic on the consumption of antidepressive agents in Central Portugal. To estimate the causal effect of the pandemic an interrupted time series analysis was conducted. Data of antidepressant drugs monthly dispensed in community pharmacies between Jan-2010 and Dec-2021 were provided by the regional Health Administration. Anti-Parkinson dopaminergic agents and statins, theoretically not influenced by COVID-19 pandemics, were used as comparator series. The number of packages was converted into defined daily doses and presented as defined daily doses/1000 inhabitants/day. A Bayesian structural time-series model with CausalImpact on R/RStudio was used to predict the counterfactual. Analyses with different geographical granularity (9 sub-regions and 78 municipalities) were performed. When compared to counterfactual, regional consumption non-significantly increased after the pandemic declaration, with a relative effect of + 1.30% [95%CI -1.6%:4.2%]. When increasing the granularity, differences appeared between sub-region with significant increases in Baixo Mondego + 6.5% [1.4%:11.0%], Guarda + 4.4% [1.1%:7.7%] or Cova da Beira + 4.1% [0.17%:8.3%], but non-significant variation in the remaining 6 sub-regions. Differences are more obvious at municipality level, ranging from increases of + 37.00% [32.00%:42.00%] to decreases of -11.00% [-17.00%:-4.20%]. Relative impact positively correlated with percentage of elderly in the municipality (r = 0.301; p = 0.007), and negatively with population density (r=-0.243; p = 0.032). No other predicting variables were found. Antidepressant consumption suffered very slight variations at regional level after the COVID-19 pandemic declaration. Analysis with higher granularity allowed identifying municipalities with higher impact (increase or decrease). The absence of clear association patterns suggests other causal hypotheses of the differences.

Non-fatal suicide behaviours across phases in the COVID-19 pandemic: a population-based study in a Catalan cohort

The COVID-19 pandemic has been extensively discussed in the context of its effect on mental health. Although global suicide rates have remained stable during the pandemic, the specific effect on non-fatal suicide behaviours during and after the pandemic remains underexplored. This study aims to investigate patterns of non-fatal suicide behaviours before, during, and after the pandemic. In this cohort study, we used data from all hospitals in Catalonia, Spain, collected through the Catalan Suicide Risk Code, which is a specifically designed suicide attempt surveillance protocol, involving a face-to-face, in-depth psychiatric evaluation, after a Catalan resident presents any suicide risk behaviour in any public health-care setting. This evaluation centralises data from suicide registries across the territory. We included non-fatal suicide behaviours, meaning suicidal ideation or attempts that did not result in death, and excluded self-harm behaviours not judged to be linked with suicidal ideation. We considered three periods: the pre-confinement period (Jan 1, 2018, to the enforcement of the lockdown in Spain on March 14, 2020); the confinement period (March 14, 2020, to the end of lockdown on June 21, 2020); and the post-confinement period (June 21, 2020, to Dec 31, 2022). We used Bayesian structural time series models to assess the effect of pandemic phases on non-fatal suicide behaviours, and we ran stratified analyses by sex and age to identify distinct patterns among demographic cohorts. We obtained 26 482 records from Jan 1, 2018, to Dec 31, 2022. The mean age was 37·94 years (SD 18·07), and the sample included 17 584 (66·4%) women and 8898 (33·6%) men. Data on ethnicity were not collected. Temporal trends showed a mild increase in non-fatal suicide behaviours from Jan 1, 2018, to March 13, 2020; a reduction during the confinement period; and a subsequent rise after confinement. Bayesian models suggested a significant causal effect of lockdown easing, resulting in a 50·77% increase in non-fatal suicide behaviours (95% credible interval [CrI] 26·62-76·58; p<0·0001). Stratified analyses indicated that the easing of lockdown resulted in a significant increase in non-fatal suicide behaviours among women (25·92%; 6·71-44·72; p=0·011) and among individuals aged 18 years and younger (72·75%; 38·81-108·11; p<0·0001). This study provides a comprehensive examination of non-fatal suicide behaviours in Catalonia, Spain, emphasising the dynamics of different COVID-19 pandemic phases. The initial reduction during strict lockdown aligns with Joiner's Interpersonal Theory of Suicide, whereas the post-confinement rise reflects complex factors, including social isolation and economic challenges. Sex-specific and age-specific analyses underscore distinct vulnerabilities, emphasising the need for targeted preventive strategies. Centro de Investigación Biomédica en Red de Salud Mental annual budget of G21, Agència de Gestió d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya. For the Catalan and Spanish translations of the abstract see Supplementary Materials section.

Comparative Analysis of ARIMA and BSTS Models for Electricity Price Forecasting in Major West Coast Cities

This study presents a detailed comparative analysis of two prominent statistical models, the AutoRegressive Integrated Moving Average (ARIMA) and Bayesian Structural Time Series (BSTS), in the context of forecasting electricity prices in major West Coast cities of the United States, namely Los Angeles, San Francisco, and Seattle. Utilizing historical electricity price data obtained from the Bureau of Labor Statistics, the research meticulously trains and tests both models, aiming to evaluate their predictive accuracy and reliability in dynamic urban energy markets. The core objective of this research is to ascertain the effectiveness of ARIMA and BSTS models in the realm of electricity price forecasting, with a particular emphasis on their applicability in urban settings characterized by rapid market evolution. Through this analysis, the study offers valuable insights that could significantly influence energy market forecasting and policy formulation. The findings are particularly relevant for stakeholders in the energy sector, including policymakers and market strategists, as they navigate the complexities and challenges of modern energy landscapes. This research contributes to a more comprehensive understanding of statistical modeling techniques in energy market analysis and provides a foundation for more informed decision-making in energy policy and strategy development.

The Iberian exception: Estimating the impact of a cap on gas prices for electricity generation on consumer prices and market dynamics

As the volatility of short-term energy market prices increases due to exogenous shocks and the changing nature of the energy mix, market interventions are gaining importance in the policy debate. Accurate and robust quantification of their impact is becoming therefore essential. This paper conducts a causality analysis to evaluate the Spanish “gas cap” for electricity generation during the 2022 energy crisis. We use Bayesian structural time series models to isolate its impact on affected consumers, primarily those under the regulated tariff, from June to December 2022. Our results show that the mechanism successfully lowered prices compared to a counterfactual scenario without the intervention. However, the policy also led to an increase in electricity generation from gas-fired combined cycle plants. In addition, exports to France increased by over 80% after implementation, as Spanish wholesale prices became cheaper than French prices. Our analysis (1) provides an accurate quantification to date of one of the most consequential energy market interventions in recent years; (2) demonstrates the fruitful use of a novel evaluation method for energy policy; (3) highlights the trade-off between the short-term goal of consumer price relief and the long-term goals of fossil fuel reduction and decarbonisation.

The Celebrity Factor: Exploring the Impact of Influencers on COVID-19 Vaccine Sentiment through Bayesian Modeling of Time Series

Online social networks allow for information to rapidly propagate throughout the world, and opinions expressed on such platforms can influence people’s decisions. During the COVID-19 pandemic, many influential public figures used these social networks to share their opinions about the vaccines developed to combat the virus. Many influencers encouraged vaccination, and a considerable number also expressed doubt and skepticism over the efficacy of the vaccines. This study modeled the impact that eleven influencers’ statements had on the overall sentiment towards COVID-19 vaccines, as expressed on Twitter. Sentiment is measured by collecting a series of publicly-available tweets made regarding the vaccine during the pandemic, and assigning each a sentiment score based on the VADER lexicon. Several models were used to analyze the impact of the influencers’ statements, including linear, sequential and tree-based models. The results were obtained by constructing a Bayesian structural time series model based on each model’s counterfactual estimate. The results found that influencers who share messages encouraging vaccination generally tend to increase the number of ”pro-vaccination” tweets over the next 20 days. Influencers sharing ”anti-vaccination” messages sometimes resulted in a decrease in anti-vaccine tweets, and other times in an increase over the next 20 days. The results from this study provide an introductory look into the complex issue of vaccine hesitancy and the effect of influencers on vaccine messaging, and inform public health strategy regarding this issue.

Exploring Public Interest in Limited-Use Areas and Compensation from Airports in Poland: A Google Trends Analysis

Abstract This article evaluates online social behavior regarding the establishment of limited-use areas (LUAs) around the airports in Warsaw and Gdansk. The study relied mainly on an analysis of Google Trends statistics, in particular the dynamics of keyword searches. The article suggests that assessments of online behavior can provide a deeper understanding of social behavior. The study involved an OLS regression analysis and a causal impact analysis of the intervention based on a Bayesian structural time-series model. This article analyses different phases of an information society's activity, from the introduction of LUAs around airports to the deadline for submitting compensation claims. The results indicate that the number of searches for the keyword "compensation" increased significantly after the introduction of LUAs and that RSV decreased after the end of the compensation process, which confirms that the intervention significantly influenced the analyzed time series.

COVID-19 impact on taxi-metro integrated usage: Changes and role of built environment, taking Lanzhou, China as an example

The COVID-19 pandemic has had a profound impact on how and how often people travel. The extent of changes in taxi-metro integrated usage (cooperative trips and competitive trips) before and after the pandemic is still unclear. Therefore, this paper aims to explore the impacts of COVID-19 on taxi-metro integrated usage and the relationship with built environment. This paper develops a novel method to infer the impact of COVID-19 on taxi-metro integrated usage and measures the resilience of cooperative and competitive ridership using a series of Bayesian structural time series models. The bootstrap aggregating regression tree model is applied to investigate the contribution of built environment on the relative impacts and resilience. Partial Dependence Plots (PDP) analysis is employed to explore the nonlinear effects of built environment elements. Results suggests: (1) The travel restrictions have a more significant impact than the number of COVID-19 cases on taxi-metro integrated usage. (2) Built environment factors contribute similarly to resisting the impact of COVID-19 on cooperative and competitive ridership during short-term and long-term period. (3) The recovery of the ridership shows a significant spatial heterogeneity. (4) Variables such as residential ratio, road network density, and bus stop density show nonlinear and threshold effects on the resilience of the ridership. Our findings have implications for travel policies during recovery from the pandemic.

Long-term impact of COVID-19 pandemic on antibiotic use in primary care: lessons to optimize antimicrobial use

ABSTRACT Background Lockdowns due to the COVID-19 pandemic was associated with changes in the pattern of use of antimicrobials, but persistence of changes after lockdowns has not been described. The objective was to describe the number of patients with dispensed antibiotic treatment and consumption of antibiotics in outpatients from primary care in Catalonia 18 months after the end of the emergency period. Research design and methods Data for the COVID-19 pandemic period was obtained from March 2020 to December 2021. Four high transmission rate (Rt > 100) periods were established. To compare data, a simple Bayesian structural time series model was used. Results The observed number of patients with dispensed antibiotics decreased respect to the estimated, especially during the four high transmission rate periods: April–May 2020 (lockdown period) (−42.57% and −42.68%); December 2020-February 2021 (−41.65%, −49.97% and −43.64%); October 2021 (−16.23%), and December 2021 (−20.16%). Overall antibiotic consumption was reduced by 23.37% (p = 0.002). These differences were mainly observed in those ≤ 15 years. Conclusions We describe the reduction in the number of patients with dispensed antibiotics and antibiotic consumption after the COVID-19 lockdown persisted in a period of recovery of healthcare accessibility. This information may help to improve antimicrobial use at the primary care level.

Causal impact analysis of weir opening on cyanobacterial blooms and water quality in the Yeongsan River, Korea: A bayesian structural time-series analysis and median difference test

The construction of weirs in Korea's Four Major Rivers Project has led to an increase in cyanobacterial blooms, posing environmental challenges. To address this, the government began opening weirs in 2017. However, interpreting experimental results has proven to be complex due to the multifaceted nature of blooms. This study aimed to assess the impact of opening the Juksan Weir on cyanobacterial blooms and water quality in the Yeongsan River. Using a median difference test (MDT) and causal impact analysis (CIA) with Bayesian structural time-series (BSTS) models, changes in cyanobacterial cell density (Cyano) and chlorophyll a concentration (Chl-a) before (January 2013 to June 2017) and after (July 2017 to December 2021) the weir-opening event were analyzed. The MDT revealed no significant change in Cyano post-weir opening (p = 0.267), but Chl-a significantly increased by 48.1 % (p < 0.01). As a result of CIA, Cyano decreased, albeit statistically insignificantly (p = 0.454), while Chl-a increased by 59.0 % (p < 0.01). These findings contradict the expectation that Cyano decrease due to the increased flow velocity resulting from weir opening. The absence of changes in Cyano and the increase in Chl-a can be attributed to several factors, including the constrained and inadequate duration of full weir opening combined with conducive conditions for the proliferation of other algae such as diatoms and green algae. These findings suggest that the effectiveness of weir opening in controlling Cyano may have been compromised by factors influencing the overall aquatic ecosystem dynamics. Further analysis revealed that factors such as elevated water temperatures (≥ 30 °C) and reduced flow rates (< 37 m3/s) contributed to the flourishing of cyanobacteria, whose concentrations exceeded 10,000 cells/mL. In analyzing causal relationships in environmental management, especially when there are complex causal interactions, the application of MDT and CIA provides valuable insights.

A Comprehensive Framework for Measuring the Immediate Impact of TV Advertisements: TV-Impact.

Measuring the immediate impact of television advertisements (TV ads) on online traffic poses significant challenges in many aspects. Nonetheless, a comprehensive consideration is essential to fully grasp consumer reactions to TV ads. So far, the measurement of this effect has not been studied to a large extent. Existing studies have either determined how a specific focus group, i.e., toddlers, people of a certain age group, etc., react to ads via simple statistical tests using a case study approach or have examined the effects of advertising with simple regression models. This study introduces a comprehensive framework called TV-Impact. The framework uses a Bayesian structural time-series model called CausalImpact. There are additional novel approaches developed within the framework. One of the novelties of TV-Impact lies in its dynamic algorithm for selecting control variables which are supporting data sources and presumed to be unaffected by TV ads. In addition, we proposed the concept of Group Ads to combine overlapping ads into a single ad structure. Then, Random Forest Regressor, which is a commonly preferred supervised learning method, is used to decompose the impact into single ads. The TV-Impact framework was applied to the data of iLab, a venture company in Turkey, and manages its companies' advertising strategies. The findings reveal that the TV-Impact model positively influenced the companies' strategies for allocating their TV advertisement budgets and increased the amount of traffic driven to company websites, serving as an effective decision support system.

Bayesian Structural Time Series Models for Predicting the $${\textrm{CO}}_2$$ Emissions in Afghanistan

Bayesian Structural Time Series Models for Predicting the $${\textrm{CO}}_2$$ Emissions in Afghanistan

Assessing the impact of COVID-19 interventions on the hand, foot and mouth disease in Guangdong Province, China: a Bayesian modeling study.

The non-pharmaceutical interventions (NPIs) against COVID-19 may have affected the transmission of hand, foot and mouth disease (HFMD). We aimed to assess the impact of the NPIs on HFMD in the high epidemic area of HFMD, Guangdong Province. The data of HFMD cases, etiological information, and meteorological factors in Guangdong from January 1, 2012, to December 31, 2021, were collected. Using a Bayesian structural time series (BSTS) model integrated counterfactual framework, we assessed the effect of NPIs on HFMD by different intervention periods, populations (gender, age, occupation), and cities. We further explored the correlation between the reduction of HFMD and socioeconomic factors in 21 cities. A total of 351,217 HFMD cases were reported and 455,327 cases were averted in Guangdong Province during 2020-2021 with a reduction of 84.94% (95%CI: 81.63-87.22%) in 2020 and 29.49% (95%CI: 15.26-39.54%) in 2021. The impact of NPIs on HFMD differed by age and gender. The effects of NPIs were more remarkable for children aged 0-2 years and scattered children. We found that the relative reductions in 21 cities were related to the composition ratio of children and COVID-19 incidence. The reduction of HFMD incidence was significantly associated with COVID-19 NPIs, and school closure was an effective intervention to prevent HFMD outbreaks. Our findings will contribute to the development of HFMD prevention and control measures.

Evaluating the effects of the 2017 National Health Insurance coverage expansion on amenable mortality and its disparities between areas in South Korea using Bayesian structural time-series models

To improve the low coverage rate of the National Health Insurance (NHI), South Korea implemented the NHI coverage expansion plan in 2017 to cover medically essential non-covered services and reduce copayment rates. This study aimed to estimate the effects of the 2017 NHI coverage expansion on amenable mortality and its disparities between areas in South Korea under a controlled interrupted time-series design using Bayesian structural time-series models. Age-standardized amenable mortality rates and rate differences (RDs) and rate ratios (RRs) between areas for amenable mortality were calculated monthly between July 2012 and December 2021 and used as the response series. The non-equivalent control series were monthly non-avoidable mortality rates and their regional disparities. After the coverage expansion, amenable mortality rates decreased for both males (−8.8%, 95% credible interval [CrI] −13.4% to −3.9%) and females (−8.3%, 95% CrI −13.4% to −2.4%), with the largest decline in the non-Seoul-Capital metropolitan area (−11.6%, 95% CrI −16.5% to −6.3%) rather than the Seoul Capital Area (−7.5%, 95% CrI −11.9% to −2.5%) and a non-significant reduction in the non-Seoul-Capital non-metropolitan area in females. RDs and RRs between areas for amenable mortality decreased non-significantly (−16.2%, 95% CrI −31.3% to 2.6% for RD and −1.2%, 95% CrI −3.7% to 1.5% for RR), except for a significant decrease in RD in males (−21.8%, 95% CrI −38.0% to −1.5%), and decreased less in females than in males. The coverage expansion was generally effective in reducing amenable mortality rates by area, but had limited effects in closing amenable mortality disparities between areas, favoring males and the non-Seoul-Capital metropolitan area. These results implied that additional measures are necessary to improve access to quality health care for females and underserved areas to enhance the effectiveness of the coverage expansion.

Bayesian Structural Time Series and Geographically Weighted Logistic Regression Modelling Impacts of COVID-19 Lockdowns on the Spatiotemporal Patterns of London’s Crimes

Given the paramount impacts of COVID-19 on people’s lives in the capital of the UK, London, it was foreseeable that the city’s crime patterns would have undergone significant transformations, especially during lockdown periods. This study aims to testify the crime patterns’ changes in London, using data from March 2020 to March 2021 to explore the driving forces for such changes, and hence propose data-driven insights for policy makers and practitioners on London’s crime deduction and prevention potentiality in post-pandemic era. (1) Upon exploratory data analyses on the overall crime change patterns, an innovative BSTS model has been proposed by integrating restriction-level time series into the Bayesian structural time series (BSTS) model. This novel method allows the research to evaluate the varied effects of London’s three lockdown periods on local crimes among the regions of London. (2) Based on the predictive results from the BSTS modelling, three regression models were deployed to identify the driving forces for respective types of crime experiencing significant increases during lockdown periods. (3) The findings solidified research hypotheses on the distinct factors influencing London’s specific types of crime by period and by region. In light of the received evidence, insights on a modified policing allocation model and supporting the unemployed group was proposed in the aim of effectively mitigating the surges of crimes in London.

Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis

BackgroundIt is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the “End viral hepatitis Strategy”.MethodsWe estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method.ResultsThe hepatitis B epidemics fluctuates irregularly during the period 2005–2007(APC = 8.7, P = 0.246) and 2015–2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18–35%) within the first three months in 2020,17% (95% CI 7.7–26%) within the first six months in 2020, and 10% (95% CI19–22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001).ConclusionThe COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.