Estimating rates of change to interpret quantitative wastewater surveillance of disease trends.

BACKGROUND: Wastewater monitoring data can be used to estimate disease trends to inform public health responses. One commonly estimated metric is the rate of change in pathogen quantity, which typically correlates with clinical surveillance in retrospective analyses. However, the accuracy of rate of change estimation approaches has not previously been evaluated. OBJECTIVES: We assessed the performance of approaches for estimating rates of change in wastewater pathogen loads by generating synthetic wastewater time series data for which rates of change were known. Each approach was also evaluated on real-world data. METHODS: Smooth trends and their first derivatives were jointly sampled from Gaussian processes (GP) and independent errors were added to generate synthetic viral load measurements; the range hyperparameter and error variance were varied to produce nine simulation scenarios representing different potential disease patterns. The directions and magnitudes of the rate of change estimates from four estimation approaches (two established and two developed in this work) were compared to the GP first derivative to evaluate classification and quantitative accuracy. Each approach was also implemented for public SARS-CoV-2 wastewater monitoring data collected January 2021-May 2023 at 25 sites in North Carolina, USA. RESULTS: All four approaches inconsistently identified the correct direction of the trend given by the sign of the GP first derivative. Across all nine simulated disease patterns, between a quarter and a half of all estimates indicated the wrong trend direction, regardless of estimation approach. The proportion of trends classified as plateaus (statistically indistinguishable from zero) for the North Carolina SARS-CoV-2 data varied considerably by estimation method but not by site. DISCUSSION: Our results suggest that wastewater measurements alone might not provide sufficient data to reliably track disease trends in real-time. Instead, wastewater viral loads could be combined with additional public health surveillance data to improve predictions of other outcomes.

Prescribing Trends for Oral Vancomycin and Fidaxomicin after Guideline and Formulary Changes in Ontario, Canada: An Interrupted Time-Series Analysis.

Background: Clostridioides difficile is a pathogen causing diarrheal illness, which can be treated with vancomycin or fidaxomicin. Objective: To evaluate changes in monthly prescription volumes for oral vancomycin and fidaxomicin in Ontario community pharmacies following implementation of the 2017 and 2021 updates to guidelines from the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) and after a 2019 provincial formulary change for vancomycin. Methods: An interrupted time-series analysis was conducted from November 2015 to October 2021 using monthly projected prescription volumes obtained from IQVIA's Compuscript database. Level and slope (trend) changes in prescribing were assessed using segmented linear regression. Results: The volume of vancomycin prescriptions increased by 74 prescriptions per month (95% confidence interval [CI] 16 to 132) following implementation of the 2017 guideline update and by 73 prescriptions per month (95% CI 13 to 133) after the 2019 formulary change; however, no statistically significant changes were observed after implementation of the 2021 guideline update. No significant trend changes were observed for fidaxomicin. Conclusion: Guidelines and formulary changes were correlated with increased volume of vancomycin prescriptions.

Contexte: Le Clostridioides difficile est un agent pathogène provoquant une maladie diarrhéique pouvant être traitée avec de la vancomycine ou de la fidaxomicine. Objectif: Évaluer les changements de volume mensuel des prescriptions de vancomycine et de fidaxomicine par voie orale dans les pharmacies communautaires de l'Ontario après la mise en œuvre des lignes directrices actualisées en 2017 et 2021 de l'Infectious Diseases Society of America (IDSA) et de la Society for Healthcare Epidemiology of America (SHEA) et à la suite d'une modification au régime d'assurance-médicaments pour la vancomycine à l'échelle provinciale en 2019. Méthodologie: Une analyse de séries chronologiques interrompues a été réalisée de novembre 2015 à octobre 2021 à l'aide des volumes mensuels de prescriptions projetés qui ont été obtenus grâce à la base de données Compuscript d'IQVIA. Les changements du volume des prescriptions et de son évolution dans le temps (le niveau et la pente, respectivement) ont été évalués à l'aide d'une régression linéaire segmentée. Résultats: Le volume des prescriptions de vancomycine a augmenté de 74 prescriptions par mois (intervalle de confiance [IC] à 95 % 16­132) après la mise en œuvre des lignes directrices actualisées en 2017; il a augmenté de 73 prescriptions par mois (IC à 95 % 13­133) après la modification du régime d'assurance-médicaments de 2019; cependant, aucun changement statistiquement significatif n'a été observé après la mise en œuvre des lignes directrices actualisées en 2021. Aucun changement significatif de tendance n'a été observé pour la fidaxomicine. Conclusion: Les lignes directrices et les modifications du régime d'assurance-médicaments étaient corrélées à une augmentation du volume des prescriptions de vancomycine.

Analysis of the impact of financial and labour uncertainty on suicide mortality in England.

This paper examines the relationship between different dimensions of economic uncertainty and suicide rates in England from 1985 to 2020, both in the short and long term. The study employs a non-linear autoregressive distributed lag framework for cointegration estimation. This approach allows testing for the existence of possible asymmetries in the response of suicide mortality to increases in economic uncertainty. Uncertainty is gauged by different proxies that allow computing financial uncertainty and labour market uncertainty indicators. The analysis is replicated by gender and across regions, controlling for unemployment and economic growth. Overall, the analysis suggests that uncertainty intensified during the first year of the COVID-19 pandemic. This is in line with the stylized facts of economic uncertainty and its pronounced role in recessions. When replicating the experiment by gender, we find that women seem to be more sensitive to changes in uncertainty. Regarding the existence of asymmetries, we found that decreases in economic uncertainty have a greater impact on suicide mortality than increases.

Mortality Associated With Infectious Diseases in Dutch Nursing Homes.

BACKGROUND: Although older people in nursing homes have a larger susceptibility to infectious diseases, the extent to which infectious diseases contribute to their mortality is unknown. Therefore, we quantified the associations between seasonal infectious diseases and all-cause mortality in Dutch nursing homes. METHODS: We analyzed time series (January 2009 to December 2021) of the weekly sentinel surveillance of infectious diseases in Dutch nursing homes. A generalized linear model with binomial distribution and identity link was used to associate the proportion of all-cause mortality with the incidence of infections: COVID-19 (2020-2021), gastroenteritis (GE), influenza-like illness (ILI), and lower respiratory tract infections (LRTIs) at 0- to 4-week time-lags (mortality incidence at 0 to 4 weeks after infections incidence). RESULTS: Over 13 years, 81 nursing homes participated, with 20 to 35 homes each year (mean: 26). A total of 11,555 all-cause deaths occurred over 1,864,667 resident weeks, averaging a mortality incidence of 6.2 per 1000 resident weeks. All 4 tested infectious diseases exhibited a significant association with all-cause mortality in nursing homes (P ≤ .01). Collectively, these infectious diseases were associated with 18.6% (95% CI, 17.8%-19.3%) of all deaths. The association between mortality and ILI was significant in 7 of 12 ILI seasons. Yearly mortality associated with the specific infectious diseases was as follows: LRTI (10.2%; 95% CI, 9.6%-10.8%), ILI (8.2%; 95% CI, 7.5%-8.9%) (over the 7 of 12 significant seasons), COVID-19 (6.5%; 95% CI, 5.4%-7.7%) (over 2019-2021 as there was no previous SARS-CoV-2 circulation), and GE (2.3%; 95% CI, 2.0%-2.5%). CONCLUSION AND IMPLICATIONS: In nursing homes, the occurrence of seasonal respiratory and gastrointestinal infections is associated with nearly one-fifth of all-cause deaths. Although infection prevention and surveillance may already be performed in some nursing homes, it is vital to implement, and enhance targeted strategies like (hand) hygiene protocols, environmental cleaning practices, reducing droplet and aerosol transmission, and vaccination to effectively address specific infections.

Advancing Cardiovascular Mortality Trend Analysis: A Machine Learning Approach to Predict Future Health Policy Needs.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Impact of federal antipsychotic use policy in nursing homes on new diagnoses for approved indications in dementia residents.

BACKGROUND: Federal policies targeting antipsychotic use among nursing home (NH) residents may have increased reporting of diagnoses for approved uses, including schizophrenia, Tourette's syndrome, and Huntington's Disease (called "exclusionary diagnoses" because they exclude residents from the antipsychotic quality metric). We assessed changes in new exclusionary diagnoses among long-stay NH admissions specifically with dementia following federal policies. METHODS: Retrospective, quarterly, interrupted time-series analysis (2009-2018) of new long-stay NH residents with dementia and no exclusionary diagnoses reported before NH admission. The National Partnership and the addition of facility level antipsychotic use to the Five Star Quality Rating system were key time exposures. Outcome was quarterly facility level predicted percentage of exclusionary diagnoses within 2 years of admission stratified by NH characteristics. RESULTS: For 264,095 long-stay admissions, mean percentage of new exclusionary diagnoses was 2.2% before the Partnership. After the Partnership, there was an unadjusted increase in the percentage over time (slope change, 0.044, p = 0.018), but the percentage never exceeded 2.9%. The Partnership contributed to a one-time decrease in diagnoses in NHs with an intermediate percentage of Black residents (-1.29%, p = 0.004). Before the Partnership, diagnoses were increasing among not-for-profit relative to for-profit NHs (0.044; p = 0.012), but after the Partnership, the pattern reversed. For-profit NHs saw an increase (+0.034, p = 0.002); not-for-profit NHs experienced a decrease (-0.014, p = 0.039). Quality Rating modifications had no significant effect. CONCLUSIONS: Exclusionary diagnosis reporting among long-stay NH residents with dementia, the group most at risk from antipsychotics, did not increase in response to federal policies. Evaluation of reasons for the observed increase in exclusionary diagnoses among non-dementia NH residents is warranted along with continued attention to how to incentivize the appropriate use of medications in residents with dementia that is crucial for high-quality NH care.

How did the COVID-19 pandemic affect antibiotic consumption within humanitarian emergencies? Results from five humanitarian contexts.

Introduction: Both high- and low-income countries reported increased antibiotic consumption among COVID-19 patients during the first months of the pandemic. To date, however, no studies have examined changes in antibiotic consumption during the COVID-19 pandemic within humanitarian emergency contexts. Method: Data was collected by Médecins Sans Frontières (MSF) for the years 2018-2021 across the following humanitarian settings: Afghanistan (Lashkar Gah), Bangladesh (Kutupalong), the Democratic Republic of Congo (Mweso and Baraka), and South Sudan (Bentiu). Inpatient and outpatient antibiotic consumption was calculated as Daily Defined Dose (DDD) per 1000 inhabitants per day, as per the World Health Organisation's (WHO) Collaborating Centre for Drug Statistics Methodology. Interrupted time series (ITS) analysis, using an autoregressive integrated moving average (ARIMA) model was used to analyse retrospective monthly antibiotic consumption. The impact of COVID-19 pandemic was evaluated as total antibiotic consumption and according to WHO Access, Watch, Reserve (AWaRe) group classifications within each humanitarian setting. Results: The COVID-19 pandemic had no statistically significant impact on total antibiotic consumption in South Sudan (Bentiu) and Bangladesh (Kutupalong). Similarly, the pandemic had no impact on total antibiotic consumption in DR Congo (Baraka), despite an initial 0.27% (estimate=.274, p-value=0.006) increase in March 2020 driven by Access group antibiotics. Meanwhile, total antibiotic consumption in DR Congo (Mweso) and Afghanistan (Lashkar Gah) declined by 0.74% (estimate = -.744, p = 0.003) and 0.26% (estimate = -.26, p < 0.001), respectively with the COVID-19 pandemic. Conclusion: Further studies are required to investigate what may have contributed to these results.

Impact of COVID-19 public health restrictions on fall-related hip fracture hospitalizations: An interrupted time series analysis.

PURPOSE: There is limited evidence regarding the impact of public health restrictions on hip fracture hospitalization by place of fracture occurrence. This study aimed to examine the impact of COVID-19 public health restrictions on fall-related hip fracture hospitalization rates by place of occurrence. METHODS: This retrospective cohort study was conducted using hospitalization data in New South Wales, Australia, between January 2014 and June 2022. Older adults aged ≥65 years admitted to hospital following a fall-related hip fracture. An interrupted time-series analysis using autoregressive integrated moving average models evaluated the impact of public health restrictions on fall-related hip fracture hospitalization by place of fracture occurrence (home/residence, residential aged care facility (RACF), or away from usual residence). RESULTS: The mean observed fall-related hip fracture hospitalization rate during COVID-19 public health restrictions (36.3 per 100,000 people per month) was 13.4 % lower than the forecasted rate (41.1 per 100,000 people per month). The mean observed hospitalization rates for fall-related hip fractures at home/residence, at a RACF, and away from the usual residence were 3.8 %, 18.5 %, and 40.1 % lower than the forecasted rates, respectively. Level changes in the fall-related hip fracture hospitalization rates at RACFs and away from usual residences were -0.9 per 100,000 people per month (95 % CI -1.6 to -0.2) and -1.7 per 100,000 people per month (95 % CI -2.5 to -0.9), respectively. CONCLUSIONS: There was a decline in fall-related hip fracture hospitalization rates among older adults, where the fracture occurred at RACFs and away from a person's usual residence during COVID-19 public health restrictions.

Forest disturbance regimes and trends in continental Spain (1985-2023) using dense landsat time series.

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.

Short-term effects of air pollutants and meteorological factors on outpatients with allergic airway disease in Ningbo, China, 2015-2021.

OBJECTIVES: The allergic airway disease, such as allergic rhinitis, chronic rhinosinusitis, asthma, is a general term of a range of inflammatory disorders affecting the upper and lower airways and lung parenchyma. This study aimed to investigate the short-term effects of air pollutants and meteorological factors on AAD-related daily outpatient visits. STUDY DESIGN: An ecological study. METHODS: Data on outpatient visits due to AAD (n = 4,554,404) were collected from the platform of the Ningbo Health Information from January 1, 2015 to December 31, 2021. A Quasi-Poisson generalized additive regression model was established to analyze the lag effects of air pollution on daily outpatient visits for AAD. Restricted cubic spline functions were used to explore the potential non-linear relationships between air pollutants and meteorological and daily outpatient visits for AAD. RESULTS: PM2.5, PM10, SO2, NO2, or CO were associated with daily outpatient visits for AAD, and there was a significant increasing trend in the cumulative lag effects. SO2 had the largest effect at Lag07, with a 25.3% (95% CI: 21.6%-29.0%) increase in AAD for every 10 µg/m3 increase in exposure concentration. Subgroup analysis showed that the 0-18 years old age group had the strongest effects, especially for AR, and all effects were stronger in the cold season. CONCLUSIONS: Given that patients aged 0-18 are more susceptible to environmental changes, protective measures specifically for children should be taken during dry and cold weather conditions with poor air quality.

Application of improved harmonic Poisson segmented regression model in evaluating the effectiveness of Kala-Azar intervention in Yangquan City, China.

Background: The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions. Methods: We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models. Results: The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (ß1 + ß3 = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively. Conclusion: In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.

Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe "Onion Clustering": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

Methadone Take-Home Policies and Associated Mortality: Permitting versus Non-Permitting States.

To mitigate COVID-19 exposure risks in methadone clinics, the Substance Abuse and Mental Health Services Administration (SAMHSA) issued a temporary modification of regulations in March 2020 to permit, with state concurrence, extended take-home methadone doses. The modification allowed for up to 28 days of take-home methadone for stable patients and 14 days for those less stable. Using both interrupted time series and difference-in-differences methods, this study examined the association between the policy change and fatal methadone overdoses, comparing states that permitted the expansion of take-home doses with states that did not. The findings suggest the pandemic emergency take-home policy did not increase methadone-involved mortality.

The associations of supervised consumption services with the rates of opioid-related mortality and morbidity outcomes at the public health unit level in Ontario (Canada): A controlled interrupted time-series analysis.

INTRODUCTION: This study aimed to assess the impact of the implementation of legally sanctioned supervised consumption sites (SCS) in the Canadian province of Ontario on opioid-related deaths, emergency department (ED) visits and hospitalisations at the public health unit (PHU) level. METHODS: Monthly rates per 100,000 population of opioid-related deaths, ED visits and hospitalisations for PHUs in Ontario between December 2013 and March 2022 were collected. Aggregated and individual analyses of PHUs with one or more SCS were conducted, with PHUs that instituted an SCS being matched to control units that did not. Autoregressive integrated moving average models were used to estimate the impact of SCS implementation on opioid-related deaths, ED visits and hospitalisations. RESULTS: Twenty-one legally sanctioned SCS were implemented across nine PHUs in Ontario during the study period. Interrupted time series analyses showed no statistically significant changes in opioid-related death rates in aggregated analyses of intervention PHUs (increase of 0.02 deaths/100,000 population/month; p = 0.27). Control PHUs saw a significant increase of 0.38 deaths/100,000 population/month; p < 0.001. No statistically significant changes were observed in the rates of opioid-related ED visits in intervention PHUs (decrease of 0.61 visits/100,000 population/month; p = 0.39) or controls (increase of 0.403 visits; p = 0.76). No statistically significant changes to the rates of opioid-related hospitalisations were observed in intervention PHUs (0 hospitalisations/100,000 population/month; p = 0.98) or controls (decrease of 0.05 hospitalisations; p = 0.95). DISCUSSION AND CONCLUSIONS: This study did not find significant mortality or morbidity effects associated with SCS availability at the population level in Ontario. In the context of a highly toxic drug supply, additional interventions will be required to reduce opioid-related harms.

Fractional-order state space reconstruction: a new frontier in multivariate complex time series.

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.

Federal opioid agonist therapy policy: interrupted time series analysis of the impact of the methadone exemption removal across eight provinces in Canada.

BACKGROUND: Federal deregulation of opioid agonist therapies are an attractive policy option to improve access to opioid use disorder care and achieve widespread beneficial impacts on growing opioid-related harms. There have been few evaluations of such policy interventions and understanding effects can help policy planning across jurisdictions. METHODS: Using health administrative data from eight of ten Canadian provinces, this study evaluated the impacts of Health Canada's decision in May 2018 to rescind the requirement for Canadian health professionals to obtain an exemption from the Canadian Drugs and Substance Act to prescribe methadone for opioid use disorder. Over the study period of June 2017 to May 2019, we used descriptive statistics to capture overall trends in the number of agonist therapy prescribers across provinces and we used interrupted time series analysis to determine the effect of this decision on the trajectories of the agonist therapy prescribing workforces. RESULTS: There were important baseline differences in the numbers of agonist therapy prescribers. The province with the highest concentration of prescribers had 7.5 more prescribers per 100,000 residents compared to the province with the lowest. All provinces showed encouraging growth in the number of prescribers through the study period, though the fastest growing province grew 4.5 times more than the slowest. Interrupted time series analyses demonstrated a range of effects of the federal policy intervention on the provinces, from clearly positive changes to possibly negative effects. CONCLUSIONS: Federal drug regulation policy change interacted in complex ways with provincial health professional regulation and healthcare delivery, kaleidoscoping the effects of federal policy intervention. For Canada and other health systems such as the US, federal policy must account for significant subnational variation in OUD epidemiology and drug regulation to maximize intended beneficial effects and mitigate the risks of negative effects.

Seasonality of common respiratory viruses: Analysis of nationwide time-series data.

BACKGROUND AND OBJECTIVE: Understanding the seasonal behaviours of respiratory viruses is crucial for preventing infections. We evaluated the seasonality of respiratory viruses using time-series analyses. METHODS: This study analysed prospectively collected nationwide surveillance data on eight respiratory viruses, gathered from the Korean Influenza and Respiratory Surveillance System. The data were collected on a weekly basis by 52 nationwide primary healthcare institutions between 2015 and 2019. We performed Spearman correlation analyses, similarity analyses via dynamic time warping (DTW) and seasonality analyses using seasonal autoregressive integrated moving average (SARIMA). RESULTS: The prevalence of rhinovirus (RV, 23.6%-31.4%), adenovirus (AdV, 9.2%-16.6%), human coronavirus (HCoV, 3.0%-6.6%), respiratory syncytial virus (RSV, 11.7%-20.1%), influenza virus (IFV, 11.7%-21.5%), parainfluenza virus (PIV, 9.2%-12.6%), human metapneumovirus (HMPV, 5.6%-6.9%) and human bocavirus (HBoV, 5.0%-6.4%) were derived. Most of them exhibited a high positive correlation in Spearman analyses. In DTW analyses, all virus data from 2015 to 2019, except AdV, exhibited good alignments. In SARIMA, AdV and RV did not show seasonality. Other viruses showed 12-month seasonality. We describe the viruses as winter viruses (HCoV, RSV and IFV), spring/summer viruses (PIV, HBoV), a spring virus (HMPV) and all-year viruses with peak incidences during school periods (RV and AdV). CONCLUSION: This is the first study to comprehensively analyse the seasonal behaviours of the eight most common respiratory viruses using nationwide, prospectively collected, sentinel surveillance data.

The impact of targeted local outreach clinics to improve COVID-19 vaccine uptake: controlled interrupted time series in South West England.

BACKGROUND: Outreach clinics were part of efforts to maximise uptake in COVID-19 vaccination. METHODS: We used controlled interrupted time series, matching on age, sex, deprivation and vaccination eligibility date, to determine the effect of outreach clinics on time to first COVID-19 vaccine, using a population-based electronic health record database of 914,478 people, from December 2020 to December 2021; people living within 1 mile of each outreach clinics were exposed. RESULTS: 50% of 288,473 exposed citizens were white British, and 71% were aged 0-49 years. There was no evidence for an overall statistically significant increase in cumulative percentage vaccinated due to the outreach clinic at 6 weeks, with an overall pooled effect estimate of -0.07% (95% CI: -1.15%, 1.02%). The pooled estimate for increased cumulative vaccine uptake varied slightly depending on how the analysis was stratified; by ethnic group it was - 0.12% (95% CI: -0.90%, 0.66%); by age group it was - 0.06% (95% CI: -0.41%, 0.28%); and by deprivation it was 0.03% (95% CI: -0.74%, 0.79%). CONCLUSIONS: Living within a mile of an outreach clinic was not associated with higher vaccine uptake. Evaluation of future outreach clinics should consider the relative importance of travel amongst other barriers to accessing vaccines.

Decreasing trend in thyroid cancer incidence: a study from central Italy (2007-2019).

PURPOSE: Due to overdiagnosis, the incidence of thyroid cancer (TC) has increased in high-income countries, including Italy. Efforts have been made to address this issue since the mid-2010s, but more information is needed about how TC incidence has changed. We aim to examine the trend in TC incidence in the Lazio Region (central Italy) and assess the impact of the 2014 Italian Consensus for the Classification and Reporting of Thyroid Cytology (ICCRTC) to identify potential changes in TC diagnosis. METHODS: To identify TC cases, we conducted a population-based study (period 2007-2019) using the data from the Lazio Region Cancer Registry (5.8 million residents). We calculated the annual age-standardized incidence rates of TC for both sexes and analyzed the impact of ICCRTC on monthly incidence rates using segmented linear regression applied to interrupted time-series (ITS). RESULTS: Throughout the 13 years, there was a significant decline in TC annual incidence rates, more pronounced in females. Our results are consistent with reports from outside Europe (United States and South Korea). Following ICCRTC implementation in 2014, a step-change reduction in both sexes was revealed. CONCLUSIONS: Our study indicates a significant decrease in the incidence of TC, particularly among females. The ITS analysis highlights the possible role of ICCRTC in reducing overdiagnosis. As the Lazio Region reflects the Italian population in terms of various demographic, health, and lifestyle indicators, our findings can be applicable at the national level.

Interrupted time series analysis of chronic periodontitis-related procedures before and after the scaling reimbursement policy in Korea.

AIM: To study the use of a quasi-experimental design to assess the effects of scaling reimbursement policies on the incidence of chronic-periodontitis procedures. MATERIALS AND METHODS: Interrupted time series analysis was used to compare the effects before and after policy implementation using data on the number of periodontitis-related procedures from the Korean National Health Insurance Service-National Sample Cohort (n = 740,467) and the Health Screening Cohort (n = 337,904). Periodontitis-related procedures with diagnosis codes were categorized into basic (scaling or root planing), intermediate (subgingival curettage) and advanced (tooth extraction, periodontal flap surgery, bone grafting for alveolar bone defects or guided tissue regeneration). Subjects' demographics and comorbidities were considered. The incidence rate of immediate changes and gradual effects before and after policy implementation was assessed. RESULTS: Following the policy implementation from July 2013, an immediate increase was observed in total and basic procedures. No significant changes were noted in intermediate and advanced procedures initially. A decrease in the slope of intermediate procedures was observed in both databases. Advanced procedures showed varied trends, with no change in the National Sample Cohort but an increase in the Health Screening Cohort, particularly among subjects with comorbidities. CONCLUSIONS: Following the new policy implementation, the number of intermediate procedures decreased while the number of advanced procedures increased, especially among patients with comorbidities. These findings offer valuable insights on policy evaluation.