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Kiwifruit (Actinidia chinensis var. chinensis) firmness is routinely measured in a destructive manner for decision-making purposes. Thus, a population's quality is inferred by measuring a sample from that population. Consequently, studies have investigated non-destructive techniques for measuring fruit firmness. However, most of these studies have been restricted to a single season or focused on performance over long-term storage. This work compared non-destructive compression (1 mm deformation) and acoustic stiffness with flesh firmness measured with a penetrometer across three seasons. 'SunGold' kiwifruit were harvested from 11, 9 and 3 orchards on multiple occasions in 2020, 2021 and 2022, respectively. Kiwifruit was freighted to Palmerston North and assessed on arrival. Thirty fruit per orchard were measured on lab arrival, whilst 24 fruit per orchard were stored for two weeks at 0°C prior to assessment. The non-destructive methods had a strong (r2 > 0.89–0.92) segmented correlation with flesh firmness (0.52–10 kgf). Flesh firmness could be adequately estimated with the non-destructive methods within a season. However, segmented regression performance was reduced when predicting for a season outside of the training population. Nonetheless, these non-destructive methods may be useful for estimating flesh firmness at harvest and after short-term storage (2 weeks at 0 °C). [ABSTRACT FROM AUTHOR]

Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel Gradient Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of Gradient Descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://doi.org/10.1016/j.jestch.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030–1038, https://doi.org/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722–62750, 2022, https://doi.org/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method "GDS-SRFFL" has overall accuracy of 89.42% as compared to other existing methods. [ABSTRACT FROM AUTHOR]

Inbreeding depression is a genetic phenomenon associated with the loss of fitness and mean phenotypic performance due to mating between relatives. Historically, inbreeding coefficients have been estimated from pedigree information. However, the onset of genomic selection programs provides large datasets of individuals genotyped using SNP arrays, enabling more precise assessment of an individual's genomic‐level inbreeding using genomic data. One of the traits most sensitive to issues stemming from increased inbreeding is reproduction. This is particularly important in equine, in which fertility is only moderate compared to other livestock species. To explore this further, we evaluated the effect of inbreeding on five reproductive traits (age at first foaling (AFF), average interval between foalings (AIF), total number of foalings (NF), productive life (PL) and reproductive efficiency (RE)) in Pura Raza Español mares using genomic data. Residual predicted phenotypes were obtained by purging these traits through the REML (wgResidual) and ssGREML (gResidual) approaches in reproductive data of 29,847 PRE mares using the BLUPF90+ program. Next, we used pedigree‐based (Fped) and ROH‐based genomic (FROH) inbreeding coefficients derived from 1018 animals genotyped with 61,271 SNPs to estimate the inbreeding depression (linear regression). Our results indicated significant levels of inbreeding depression for all reproductive traits, with the exception of the AIF trait when Fped was used. However, all traits were negatively affected by the increase in genomic inbreeding, and FROH was found to capture more inbreeding depression than Fped. Likewise, REML models (ssGREML) using genomic data for estimated predicted residual phenotypes resulted in higher variance explained by the model compared with the models not using genomics (REML). Finally, a segmented regression analysis was conducted to evaluate the effect of inbreeding depression, revealing that the levels of genealogical and genomic homozygosity do not manifest uniformly in reproductive traits. In contrast, the levels of inbreeding depression ranged from low to high as homozygosity increased. This analysis also showed that reproductive traits are very sensitive to inbreeding depression, even with relatively low levels of homozygosity. [ABSTRACT FROM AUTHOR]

Segmented regression is widely used in many disciplines, especially when dealing with environmental data. This paper deals with the problem of selecting the correct number of changepoints in segmented regression models. A review of the usual selection criteria, namely information criteria and hypothesis testing, is provided. We enhance the latter method by proposing a novel sequential hypothesis testing procedure to address this problem. Our sequential procedure's performance is compared to methods based on information-based criteria through simulation studies. The results show that our proposal performs similarly to its competitors for the Gaussian, Binomial, and Poisson cases. Finally, we present two applications to environmental datasets of crime data in Valencia and global temperature land data. [ABSTRACT FROM AUTHOR]

Abstract Introduction Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. Objective To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. Methods A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. Results We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. Conclusion The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

Abstract Background The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data. Methods We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods. Results Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method. Conclusions Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

A pandemic crisis like the Covid-19 outbreak is a complex event, involving numerous aspects of the social life on multiple temporal scales. Focusing on the Spanish Twittersphere, we characterized users' activity behavior across the different phases of the Covid-19 first wave. Firstly, we analyzed a sample of timelines of different classes of users from the Spanish Twittersphere in terms of their propensity to produce new information or to amplify information produced by others. Secondly, by performing stepwise segmented regression analysis and Bayesian switchpoint analysis, we looked for a possible behavioral footprint of the crisis in the statistics of users' activity. We observed that generic Spanish Twitter users and journalists experienced an abrupt increment of their tweeting activity between March 9 and 14, in coincidence with control measures being announced by regional and state-level authorities. However, they displayed a stable proportion of retweets before and after the switching point. On the contrary, politicians represented an exception, being the only class of users not experimenting this abrupt change and following a completely endogenous dynamics determined by institutional agenda. On the one hand, they did not increment their overall activity, displaying instead a slight decrease. On the other hand, in times of crisis, politicians tended to strengthen their propensity to amplify information rather than produce it. [ABSTRACT FROM AUTHOR]

Introduction: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. Objective: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. Methods: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. Results: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. Conclusion: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study. [ABSTRACT FROM AUTHOR]

Background: In the Americas, deaths by diseases avoidable with vaccines are a significant contributor to child mortality. An essential means of reducing this is through broad vaccine coverage. The COVID-19 pandemic has posed a potential disruption to vaccine coverage due to its effects on the healthcare system. Objectives: this study aims to evaluate the impact of the COVID-19 pandemic on DTP3 vaccination coverage in the Americas, investigating trends from 2012 to 2022 to identify significant changes, regional disparities, and the overall effect of the pandemic on progress towards global immunization targets. Methods: This study used the coverage data for the third dose of the diphtheria, tetanus, and pertussis vaccine (DTP3) pulled from UNICEF databases spanning 2012 to 2022. We conducted a Joinpoint regression to identify points of significant trend changes. The annual percentage change (APC) and 95% confidence intervals (95% CIs) were calculated for America and its regions. We also used segmented regression analysis. Using the Chi-square test, we compared DTP3 vaccination coverage for each country between 2019 and 2022. Results: Overall, America saw a decrease in vaccine coverage during this period, with an APC of −1.4 (95% CI −1.8; −1.0). This trend varied across regions. In North America, the decrease was negligible (−0.1% APC). South America showed the steepest decrease, with an APC of −2.5%. Central America also declined, with an APC of −1.3%. Our findings suggest a concerning trend of declining DTP-vaccination rates in the Americas, exacerbated in certain regions, in the wake of the COVID-19 pandemic. The absolute decrease in vaccine coverage in the Americas was −4% between 2019 and 2022, with the most important drop being in Central America (−7%). However, six countries reported increased vaccination rates post-COVID-19, led by Brazil, with a 7% increase. Conversely, twenty-two countries registered a decline in DTP3 vaccine coverage, with the average decrease being −7.37%. This decline poses an important challenge to achieving the WHO's target of 90% coverage for the third dose of DTP by 2030, as evidenced by the reduction in the number of countries meeting this target from 2019 to 2022. Conclusions: The COVID-19 pandemic has impacted vaccine coverage in America, leading to a decrease, especially across Central America. [ABSTRACT FROM AUTHOR]

Background: The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data. Methods: We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods. Results: Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method. Conclusions: Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice. [ABSTRACT FROM AUTHOR]

Background: Elective surgeries were suspended during the national lockdown in March 2020 to curb the spread of the COVID-19 pandemic in Malaysia. We sought to evaluate the impact of the lockdown on cataract surgeries and suggest lessons for future outbreaks. Study design: We conducted an interrupted time series analysis to examine rates of cataract surgery before and during the lockdown. Methods: We used national cataract surgical data between 2015 and 2021 from the Malaysian Cataract Surgery Registry. Segmented regression with a seasonally adjusted Poisson model was used for the analysis. Stratified analyses were performed to establish whether the effect of the lockdown on cataract surgeries varied by hospital designation, type of cataract service, sex, and age groups. Results: Cataract surgeries began falling in March 2020 at the onset of the lockdown, reached a trough in April 2020, and subsequently increased but never recovered to pre-lockdown levels. Cataract surgical rates in December 2021 were still 43 % below the expected surgical volume, equivalent to 2513 lost cataract surgeries. There was no evidence of a differential effect of the lockdown between COVID-19 designated and non-COVID-19 designated hospitals. The relative decrease in cataract surgical rates appears to have been greatest in outreach services and in people 40 years and older. Conclusions: The lockdown caused an immediate reduction in cataract surgical rates to nearly half of its baseline rate. Despite its gradual recovery, further delays remain to be expected should there be no redistribution or increase in resources to support backlogs and incoming new cases.

Abstract The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions. To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models. OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates. Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model.

Recently, segmented regression has been utilized as a "working" model for a bootstrap test to detect true oxygen uptake plateau. This approach employs an iterative procedure based on least squares to fit the model. However, it is widely acknowledged that the least squares approach is highly sensitive to outliers, often yielding inefficient estimates. This paper proposes an alternative iterative method by substituting the least squares step with a M-estimator step. Leveraging the robust features of M-estimators, the proposed method is expected to exhibit resilience against outliers. To empirically investigate the performance of the proposed method, a Monte Carlo simulation study is conducted. For comparison purposes, the classical iterative method is also considered. The results indicate that, in uncontaminated scenarios, both methods exhibit similar behavior, with the classical method presenting a slightly superior performance. However, in contaminated cases, the classical method is highly deteriorated, with significant bias and root mean squared error, while the proposed method demonstrates much better performance. Both methods are employed to fit the "working" 3-segment regression model and perform a plateau test on apparently contaminated oxygen uptake data. The results reveal that, due to the presence of outliers, the classical method produces inflated estimates for the error variance and larger standard errors for all parameters, in comparison to the proposed method. Moreover, the plateau test conducted using the classical (robust) method leads to the rejection (confirmation) of a plateau of oxygen uptake for the same data. Overall, these findings highlight the superiority of the proposed method in handling outliers. [ABSTRACT FROM AUTHOR]

Background: Studies show conflicting evidence on the association of age and occupational injury in agriculture, and few studies have addressed the effect of age on work-related chronic conditions or preventive practices among farmers. Methods: We examined the probability of injury, work-related stress, musculoskeletal symptoms (MSS), and preventive practices for MSS as a function of aging using surveillance data of 7,711 farm and ranch operators in the central United States. Findings: Segmented regression analyses of men (85% of sample) indicated that the probability of all four outcomes increased up to a certain age and then decreased; the changepoints in years of age being 59.6 for injury, 55.4 for work-related stress, 59.6 for MSS, and 67.9 for MSS preventive practices. Female operators had an increasing trend for stress up to age 29.7, while they showed no changepoints across their age spectrum in the proportion of injury, MSS, and prevention techniques. Conclusion/Application to Practice: These findings emphasize the need for preventive efforts particularly among younger and middle-aged farmers and ranchers, and the need to modify work duties to match work abilities at older ages. [ABSTRACT FROM AUTHOR]

The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions. To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models. OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates. Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model. [ABSTRACT FROM AUTHOR]

Interrupted time series (ITS) are often meta‐analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta‐analysis in this context is limited. We simulated meta‐analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta‐analysed the immediate level‐ and slope‐change effect estimates using fixed‐effect and (multiple) random‐effects meta‐analysis methods. Simulation design parameters included varying series length; magnitude of lag‐1 autocorrelation; magnitude of level‐ and slope‐changes; number of included studies; and, effect size heterogeneity. All meta‐analysis methods yielded unbiased estimates of the interruption effects. All random effects meta‐analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta‐analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance. [ABSTRACT FROM AUTHOR]

Heather D Anderson,1 Vanessa Paul Patterson,1 Garth Wright,1 Julia E Rawlings,1 Gina D Moore,1 Jim Leonard,2 Robert L Page II1 1Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Anschutz Medical Campus, Aurora, CO, USA; 2Colorado Department of Health Care Policy and Financing, Denver, CO, USACorrespondence: Robert L Page II, Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, 12850 E. Montview Blvd, Mail Stop C238, Aurora, CO, 80045, USA, Tel +1 303-724-2616, Fax +1 303-724-2627, Email robert.page@cuanschutz.eduObjective: COVID-19, coinciding with the opioid epidemic in the United States, has had significant impacts on health-care utilization. While mixed, early analyses signaled a potential resurgence in opioid use following the pandemic. The primary study objective was to assess the association of the COVID-19 pandemic with opioid utilization among Health First Colorado (Colorado’s Medicaid Program) members and a non-Medicaid managed care cohort who did not have a diagnosis of cancer or sickle cell disease.Patients and Methods: Using an interrupted time series and segmented regression analysis, this population-level study assessed the association of the COVID-19 pandemic on prescribed utilization of long- and short-acting opioid analgesics among Health First Colorado members and a random sample of non-Medicaid managed care members. Pharmacy claims data for both cohorts were assessed between October 1, 2018, and September 30, 2021, with April 2020 identified as the interruption of interest. We evaluated the following monthly opioid use measures separately for short-acting and long-acting opioids: number of members filling an opioid, total fills, and total days supplied.Results: Short- and long-acting opioid utilization was significantly decreasing among Health First Colorado members in the 18 months prior to the start of COVID-19. After the onset of the pandemic, utilization stabilized and slopes were not significantly different from zero. Among the non-Medicaid managed care cohort, short- and long-acting opioid utilization significantly decreased in the 18 months leading up to the onset of the pandemic. After the onset of the pandemic, utilization of long-acting opioids stabilized, while utilization of short-acting opioids significantly increased.Conclusion: While we observed an increase in opioid utilization measures post-pandemic in the non-Medicaid managed care cohort, a similar increase was not observed in Health First Colorado members suggesting that thoughtful opioid policies put in place pre-pandemic may have been effective at controlling potential inappropriate opioid utilization.Keywords: interrupted time series, segmented regression, pain management, health care utilization, Medicaid, managed care, opioids

Abstract Background The interrupted time series (ITS) design is a widely used approach to examine the effects of interventions. However, the classic segmented regression (CSR) method, the most popular statistical technique for analyzing ITS data, may not be adequate when there is a transitional period between the pre- and post-intervention phases. Methods To address this issue and better capture the distribution patterns of intervention effects during the transition period, we propose using different cumulative distribution functions in the CSR model and developing corresponding optimized segmented regression (OSR) models. This study illustrates the application of OSR models to estimate the long-term impact of a national free delivery service policy intervention in Ethiopia. Results Regardless of the choice of transition length ( $$L$$ L ) and distribution patterns of intervention effects, the OSR models outperformed the CSR model in terms of mean square error (MSE), indicating the existence of a transition period and the validity of our model’s assumptions. However, the estimates of long-term impacts using OSR models are sensitive to the selection of L, highlighting the importance of reasonable parameter specification. We propose a data-driven approach to select the transition period length to address this issue. Conclusions Overall, our OSR models provide a powerful tool for modeling intervention effects during the transition period, with a superior model fit and more accurate estimates of long-term impacts. Our study highlights the importance of appropriate statistical methods for analyzing ITS data and provides a useful framework for future research.

Abstract Aims To evaluate the extent to which publication of high-quality randomised controlled trials(RCTs) in 2018 was associated with a change in volume or trend of subacromial decompression(SAD) surgery in patients with subacromial pain syndrome(SAPS) treated in hospitals across various countries. Methods Routinely collected administrative data of the Global Health Data@work collaborative were used to identify SAPS patients who underwent SAD surgery in six hospitals from five countries (Australia, Belgium, Netherlands, United Kingdom, United States) between 01/2016 and 02/2020. Following a controlled interrupted time series design, segmented Poisson regression was used to compare trends in monthly SAD surgeries before(01/2016-01/2018) and after(02/2018-02/2020) publication of the RCTs. The control group consisted of musculoskeletal patients undergoing other procedures. Results A total of 3.046 SAD surgeries were performed among SAPS patients treated in five hospitals; one hospital did not perform any SAD surgeries. Overall, publication of trial results was associated with a significant reduction in the trend to use SAD surgery of 2% per month (Incidence rate ratio (IRR) 0.984[0.971–0.998]; P = 0.021), but with large variation between hospitals. No changes in the control group were observed. However, publication of trial results was also associated with a 2% monthly increased trend (IRR 1.019[1.004–1.034]; P = 0.014) towards other procedures performed in SAPS patients. Conclusion Publication of RCT results was associated with a significantly decreased trend in SAD surgery for SAPS patients, although large variation between participating hospitals existed and a possible shift in coding practices cannot be ruled out. This highlights the complexities of implementing recommendations to change routine clinical practice even if based on high-quality evidence.

ObjectiveHepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends.MethodThe datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups.ResultsThe results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30–50, 51–80, and 15–29 age groups having higher growth rates.ConclusionChange point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

High‐throughput biological experiments are essential tools for identifying biologically interesting candidates in large‐scale omics studies. The results of a high‐throughput biological experiment rely heavily on the operational factors chosen in its experimental and data‐analytic procedures. Understanding how these operational factors influence the reproducibility of the experimental outcome is critical for selecting the optimal parameter settings and designing reliable high‐throughput workflows. However, the influence of an operational factor may differ between strong and weak candidates in a high‐throughput experiment, complicating the selection of parameter settings. To address this issue, we propose a novel segmented regression model, called segmented correspondence curve regression, to assess the influence of operational factors on the reproducibility of high‐throughput experiments. Our model dissects the heterogeneous effects of operational factors on strong and weak candidates, providing a principled way to select operational parameters. Based on this framework, we also develop a sup‐likelihood ratio test for the existence of heterogeneity. Simulation studies show that our estimation and testing procedures yield well‐calibrated type I errors and are substantially more powerful in detecting and locating the differences in reproducibility across workflows than the existing method. Using this model, we investigated an important design question for ChIP‐seq experiments: How many reads should one sequence to obtain reliable results in a cost‐effective way? Our results reveal new insights into the impact of sequencing depth on the binding‐site identification reproducibility, helping biologists determine the most cost‐effective sequencing depth to achieve sufficient reproducibility for their study goals. [ABSTRACT FROM AUTHOR]

Confronted with growing public concern about violence and other serious crime committed by outlaw motorcycle clubs, in 2012 the Dutch government launched a whole-of-government approach to discourage club membership and organized criminal behaviour. The whole-of-government approach included a zero tolerance policy towards crimes committed by outlaw bikers and increased law enforcement and prosecutorial attention towards members of outlaw motorcycle groups (OMCG members) and their support clubs. In this study, we estimate the effects of the whole-of-government approach on the level of prosecutorial charges levied against the Dutch biker population. We do so by applying (quasi-experimental) interrupted time series analysis to the conviction data available on 1617 Dutch OMCG members and 473 support club members in four recorded crime categories: overall crime, violent crime, organized crime and traffic offences. Although caveats remain, results indicate that the whole-of-government approach has a causal effect on the criminal involvement of OMCG and support club members, but that the nature of this effect depends on the type of crime and the subsample in question. Overall crime in the total sample seems unaffected by the approach, whereas organized crime committed by OMCG members is shown to decrease. We discuss whether the patterns observed are due to behavioural changes in OMCG and support club members, or whether they result from changes in police practices and, consequently, a changing dark figure of crime. We conclude our article with some reflections on future research. [ABSTRACT FROM AUTHOR]

Emotional intelligence, or the ability to perceive, interpret, and regulate emotions, has been linked to numerous adaptive outcomes. However, recent investigations have demonstrated that emotional intelligence can exert a nonlinear impact on motivational, interpersonal, and affective factors. Although past work has provided evidence of optimal levels of emotional intelligence, many studies have relied on statistical procedures that are prone to inferential errors when used to investigate curvilinear relationships. Thus, the current study was designed to test for the presence of a nonlinear association between emotional intelligence and generalized anxiety using an inferential method designed to detect curvilinear relationships between variables. University students (N = 345; 68.79% Caucasian, 86.99% female, X ̅Age = 26.11) completed the Brief Emotional Intelligence Scale and Zung Self-Rating Anxiety Scale. Using segmented regression analysis, we determined that the relationship between emotional intelligence and anxiety follows a nonlinear trend. Specifically, our results indicate that emotional intelligence is positively related to anxiety among those with 'low' and 'high' levels of emotional intelligence. Additionally, our findings revealed emotional intelligence and anxiety were negatively related among those with 'moderate' levels of emotional intelligence. We believe the current work has important implications for efforts designed to support university students' emotional growth and academic success. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the extent to which publication of high-quality randomised controlled trials(RCTs) in 2018 was associated with a change in volume or trend of subacromial decompression(SAD) surgery in patients with subacromial pain syndrome(SAPS) treated in hospitals across various countries. Methods: Routinely collected administrative data of the Global Health Data@work collaborative were used to identify SAPS patients who underwent SAD surgery in six hospitals from five countries (Australia, Belgium, Netherlands, United Kingdom, United States) between 01/2016 and 02/2020. Following a controlled interrupted time series design, segmented Poisson regression was used to compare trends in monthly SAD surgeries before(01/2016-01/2018) and after(02/2018-02/2020) publication of the RCTs. The control group consisted of musculoskeletal patients undergoing other procedures. Results: A total of 3.046 SAD surgeries were performed among SAPS patients treated in five hospitals; one hospital did not perform any SAD surgeries. Overall, publication of trial results was associated with a significant reduction in the trend to use SAD surgery of 2% per month (Incidence rate ratio (IRR) 0.984[0.971–0.998]; P = 0.021), but with large variation between hospitals. No changes in the control group were observed. However, publication of trial results was also associated with a 2% monthly increased trend (IRR 1.019[1.004–1.034]; P = 0.014) towards other procedures performed in SAPS patients. Conclusion: Publication of RCT results was associated with a significantly decreased trend in SAD surgery for SAPS patients, although large variation between participating hospitals existed and a possible shift in coding practices cannot be ruled out. This highlights the complexities of implementing recommendations to change routine clinical practice even if based on high-quality evidence. [ABSTRACT FROM AUTHOR]

Vaccination campaigns have been rolled out in most countries to increase vaccination coverage and protect against case mortality during the ongoing pandemic. To evaluate the effectiveness of COVID-19 vaccination, it is vital to disentangle the herd effect from the marginal effect and parameterize them separately in a model. To demonstrate this, we study the relationship between the COVID-19 vaccination coverage and case fatality rate (CFR) based on U.S. vaccination coverage at county level, with daily records from 11 March 2021 to 26 January 2022 for 3109 U.S. counties. Using segmented regression, we discovered three breakpoints of the vaccination coverage, at which herd effects could potentially exist. Controlling for county heterogeneity, we found the size of the marginal effect was not constant but actually increased as the vaccination coverage increased, and only the herd effect at the first breakpoint to be statistically significant, which implied an indirect benefit of vaccination may exist at the early stage of a vaccination campaign. Our results demonstrated that public-health researchers should carefully differentiate and quantify the herd and marginal effects when analyzing vaccination data, to better inform vaccination-campaign strategies as well as evaluate vaccination effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: Drug utilization researchers are often interested in evaluating prescribing and medication use patterns and trends over a specified period of time. Joinpoint regression is a useful methodology to identify any deviations in secular trends without a preconceived notion of where these break points might occur. This article provides a tutorial on the use of joinpoint regression, within Joinpoint software, for the analysis of drug utilization data. Methods: The statistical considerations for whether a joinpoint regression analytical technique is a suitable approach are discussed. Then, we offer a tutorial as an introduction on conducting joinpoint regression (within Joinpoint software) through a step‐by‐step application, which is a case study developed using opioid prescribing data from the United States. Data were obtained from public files available through the Centers for Disease Control and Prevention from 2006 to 2018. The tutorial provides parameters and sample data needed to replicate the case study and it concludes with general considerations for the reporting of results using joinpoint regression in drug utilization research. Results: The case study evaluated the trend of opioid prescribing in the United States from 2006 to 2018, where time points of significant variation (one in 2012 and another in 2016) are detected and interpreted. Conclusions: Joinpoint regression is a helpful methodology for drug utilization for the purposes of conducting descriptive analyses. This tool also assists with corroborating assumptions and identifying parameters for fitting other models such as interrupted time series. The technique and accompanying software are user‐friendly; however, researchers interested in using joinpoint regression should exercise caution and follow best practices for correct measurement of drug utilization. [ABSTRACT FROM AUTHOR]

Abstract Background A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions. Methods Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models. Results When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series. Conclusion Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models).

Purpose: Develop and test a flexible, scalable tool using interrupted time series (ITS) analysis to assess the impact of Food and Drug Administration (FDA) regulatory actions on drug use. Methods: We applied the tool in the Sentinel Distributed Database to assess the impact of FDA's 2010 drug safety communications (DSC) concerning the safety of long‐acting beta2‐agonists (LABA) in adult asthma patients. We evaluated changes in LABA use by measuring the initiation of LABA alone and concomitant use of LABA and asthma controller medications (ACM) after the DSCs. The tool generated ITS graphs and used segmented regression to estimate baseline slope, level change, slope change, and absolute and relative changes at up to two user‐specified time point (s) after the intervention. We tested the tool and compared our results against prior analyses that used similar measures. Results: Initiation of LABA alone declined among asthma patients aged 18–45 years before FDA DSCs (−0.10% per quarter; 95%CI: −0.11% to −0.09%) and the downward trend continued after. Concomitant use of LABA and ACM was stable before FDA DSCs. After FDA DSCs, there was a small trend decrease of 0.006% per quarter (95% CI, −0.008% to −0.003%). We found similar results among those aged 46–64 years and patients with poorly‐controlled asthma. Our results were consistent with previous studies, confirming the performance of the new tool. Conclusions: We developed and tested a reusable ITS tool in real‐world databases formatted to the Sentinel Common Data Model that can assess the impact of regulatory actions on drug use. [ABSTRACT FROM AUTHOR]

Featured Application: Roberto Cameriere and Ivan Galić equally contributed to this paper. This paper aims to propose a statistical model to assess pubertal growth spurt using the ratio of the anterior height projection to the posterior (Vba) of the fourth cervical vertebra body (C4) on cephalograms and to calculate the residual proportion of skeletal maturation and the time for the pubertal growth spurt to end for a given Vba. A sample of 538 cephalograms from healthy-living children aged between 5 and 15 years was analyzed. A segmented regression model was used to explain the different Vba stages relative to the pubertal growth spurt. In addition, the time to achieve skeletal maturation was evaluated for a given Vba between the beginning (Vba1) and the end (Vba2) of the pubertal growth spurt. A longitudinal sample of 25 males and 25 females was analyzed to validate the proposed method. The values of Vba corresponding to higher pubertal development rate ranged from Vba1 = 0.677 (95%CI, 0.644–0.711) to Vba2 = 0.966 (95%CI, 0.905–1.028) and from Vba1 = 0.669 (95%CI, 0.645–0.693) to Vba2 = 1.073 (95%CI, 1.044–1.101) in males and females, respectively. The validation process results showed that our model did not produce any incorrect forecasts. The proposed method estimates the beginning and the end of the pubertal growth spurt together with the residual proportion of skeletal maturation for a given Vba. [ABSTRACT FROM AUTHOR]

Species range shifts and expansion are subjects of primary research interest in the context of climate warming and biological invasions. Few studies have focused on reexpansion of species that suffered severe declines. Here, we focused on population recovery of Eurasian otters (Lutra lutra) in Italy, first detected in 2003 after a southward range contraction. We modeled the rate of range expansion and occupancy at the northern expanding front (central Italy), to gain insights into the progress of recovery and mechanisms of reexpansion. We performed a field survey in 2021, which redefined the northern limit of distribution further north, in close proximity to the Gran Sasso National Park. Then we analyzed a time series (1985–2021) of distances of northernmost occurrences from the center of the 1985 range. Using segmented regression, we were able to identify a prolonged stasis of the northern range edge and a simultaneous increase in occupancy from 0.151 to 0.4. A breakpoint was estimated in 2006, after which the range expanded northwards at an average rate of 5.48 km/year. From 2006 to 2021, the overall northward shift was about 80 km. Occupancy continued to increase until 2019 and abruptly declined in 2021. These patterns suggest that the reexpansion of the range can be limited by low occupancy at the expanding front. As occupancy increases, long‐distance dispersal increases and then range expands. The low occupancy at the current distribution limit of otters may reflect a higher anthropogenic pressure on northern habitats, which could slow down the reexpansion process. [ABSTRACT FROM AUTHOR]

Segmented regression is one of the statistical methods that are used to represent and model phenomena that go through several transitional stages. The basic concept of segmented regression depends on the presence of a certain point in the explanatory variable called (change point), and this point is shared and continuous between the ends of each two Consecutive transitional stages.In this research, the segmented regression model with multiple change points will be dealt with. the maximum likelihood estimator (MLE) and the two methods of robust repetitive weights (IRWm), (IRWs) will be used to estimate the model parameters and change points and then compare between these methods to choose the best method between them.A simulation process will be created with several scenarios, with different sample sizes and contamination rates (Outliers values) (15%, 10%, 5%, 0%) to Procedure the comparison process and find the best method for estimation.The simulation results, after completing the comparison process using the comparison standard, mean squared error (MSE), showed the efficiency of the Iterative Weights method (IRWm) when there were contamination rates in the data, and the efficiency of the maximum likelihood method (MLE) when the data did not contain any contamination.

The delivery and assessment of quality health care is complex with many interacting and interdependent components. In terms of research design and statistical analysis, this complexity and interdependency makes it difficult to assess the true impact of interventions designed to improve patient health care outcomes. Interrupted time series (ITS) is a quasi-experimental design developed for inferring the effectiveness of a health policy intervention while accounting for temporal dependence within a single system or unit. Current standardized ITS methods do not simultaneously analyze data for several units nor are there methods to test for the existence of a change point and to assess statistical power for study planning purposes in this context. To address this limitation, we propose the "Robust Multiple ITS" (R-MITS) model, appropriate for multiunit ITS data, that allows for inference regarding the estimation of a global change point across units in the presence of a potentially lagged (or anticipatory) treatment effect. Under the R-MITS model, one can formally test for the existence of a change point and estimate the time delay between the formal intervention implementation and the over-all-unit intervention effect. We conducted empirical simulation studies to assess the type one error rate of the testing procedure, power for detecting specified change-point alternatives, and accuracy of the proposed estimating methodology. R-MITS is illustrated by analyzing patient satisfaction data from a hospital that implemented and evaluated a new care delivery model in multiple units.

Understanding how a society views certain policies, politicians, and events can help shape public policy, legislation, and even a political candidate's campaign. This paper focuses on using aggregated, or interval censored, polling data to estimate the times when the public opinion shifts on the US president's job approval. The approval rate is modelled as a Poisson segmented (joinpoint) regression with the EM algorithm used to estimate the model parameters. Inference on the change points is carried out using BIC based model averaging. This approach can capture the uncertainty in both the number and location of change points. The model is applied to president Trump's job approval rating during 2020. Three primary change points are discovered and related to significant events and statements. [ABSTRACT FROM AUTHOR]

Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous-valued outcomes. We propose the "Generalized Robust ITS" (GRITS) model appropriate for outcomes whose underlying distribution belongs to the exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient falls from a hospital that implemented and evaluated a new care delivery model in multiple units. [ABSTRACT FROM AUTHOR]

Background: Regular measurement of the availability and use of key medicines for non-communicable diseases allows the tracking of progress to achieve equitable access to medicines. Using a country-level public sector monitoring system for medicine supply, we aim to evaluate the availability and use of losartan 50 mg tablets and metformin 850 mg tablets between 2015 and 2020 investigating the impact of different policy changes and the COVID-19 pandemic. Methods: Data from the Peruvian National System of Medicine Supply were analyzed using an interrupted time series analysis with known and unknown structural breaks. The outcomes assessed were medicine use (monthly doses dispensed at facilities over time) and medicine availability (proportion of facilities that reported having the medicine available). Results: The use of losartan and metformin at the primary level of care had a linear increasing trend over the period of analysis. In secondary and tertiary levels of care, there were no increases but some significant level and trend changes of doses dispensed at different times between 2017 to 2019, but none were related to the change of procurement procedures. At all levels of care, the COVID-19 onset in April 2020 caused an abrupt drop in doses dispensed especially at the primary level. Regarding availability, we found an increasing linear trend in the primary level of care for both medicines. In secondary and tertiary levels of care, the availability fluctuated between 40 and 95%. The onset of the COVID-19 pandemic did not significantly impact medicine availability, except for losartan in the tertiary level of care. Conclusion: The availability and proper dispensing of first-line medicines for hypertension and diabetes is an essential factor for sustainable and equitable treatment. Health care systems need to be prepared for forecasting the increasing demand of medicines for chronic diseases, but also to maintain effective medicine supply chains during humanitarian crisis like pandemics. [ABSTRACT FROM AUTHOR]

The main purpose of mathematical model building while employing statistical data analysis is to obtain high accuracy of approximation within the range of observed data and sufficient predictive properties. One of the methods for creating mathematical models is to use the techniques of regression analysis. Regression analysis usually applies single polynomial functions of higher order as approximating curves. Such an approach provides high accuracy; however, in many cases, it does not match the geometrical structure of the observed data, which results in unsatisfactory predictive properties. Another approach is associated with the use of segmented functions as approximating curves. Such an approach has the problem of estimating the coordinates of the breakpoint between adjacent segments. This article proposes a new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage. The proposed method is explained by calculation examples obtained using statistical simulation and real data observation. [ABSTRACT FROM AUTHOR]

Abstract Higher availability of administrative data and better infrastructure for electronic surveys allow for large sample sizes in evaluations of national and other large scale policies. Although larger datasets have many advantages, the use of big disaggregate data (e.g., on individuals, households, stores, municipalities) can be challenging in terms of statistical inference. Measurements made at the same point in time may be jointly influenced by contemporaneous factors and produce more variation across time than suggested by the model. This excess variation, or co-movement over time, produce observations that are not truly independent (i.e., cross-sectional dependence). If this dependency is not accounted for, statistical uncertainty will be underestimated, and studies may indicate reform effects where there is none. In the context of interrupted time series (segmented regression), we illustrate the potential for bias in inference when using large disaggregate data, and we describe two simple solutions that are available in standard statistical software.

• Machine learning methods such as Random Forest, XGBoost, and GRNN are applied to the bias correction of sea ice thickness products such as PIOMAS, APP-x and CS2SMOS. • Combined with knowledge-driven and data-driven schemes, key marine and atmospheric environmental factors affecting changes in sea ice thickness were screened out. • The sea and air environment, geography, and time information affecting sea ice thickness are combined with a machine learning algorithm to effectively improve the effect of sea ice thickness bias correction. • According to the characteristics of sea ice thickness products, a two-step regression scheme that divides sea ice into thick ice and thin ice can improve the effect of bias correction. With the accelerated melting of Arctic sea ice in the context of climate change, accurate sea ice thickness observations are needed for long-term climate simulation, short-term prediction, and shipping navigation. Limited by their special geographical location, in-situ data of Arctic sea ice thickness is scarce, and the quality of observation products from existing satellites and models is uneven. In this situation, we selected the key meteorological and marine environmental factors that affect the thickness of sea ice based on the thermodynamic law of sea ice, the traditional method of causal analysis, and the data mining ability of machine learning. Then, we constructed bias correction models combining these factors with geographic and temporal information, integrating random forest, extreme gradient boosting tree, and generalised regression neural network to correct the sea ice thickness data. The segmented regression models of thick and thin ice were constructed according to the characteristics of sea ice thickness products. The results show that the proposed bias correction model of sea ice thickness can effectively improve the quality of existing products and reduce the bias from the in-situ data. [ABSTRACT FROM AUTHOR]

Background Regular measurement of the availability and use of key medicines for non-communicable diseases allows the tracking of progress to achieve equitable access to medicines. Using a country-level public sector monitoring system for medicine supply, we aim to evaluate the availability and use of losartan 50 mg tablets and metformin 850 mg tablets between 2015 and 2020 investigating the impact of different policy changes and the COVID-19 pandemic. Methods Data from the Peruvian National System of Medicine Supply were analyzed using an interrupted time series analysis with known and unknown structural breaks. The outcomes assessed were medicine use (monthly doses dispensed at facilities over time) and medicine availability (proportion of facilities that reported having the medicine available). Results The use of losartan and metformin at the primary level of care had a linear increasing trend over the period of analysis. In secondary and tertiary levels of care, there were no increases but some significant level and trend changes of doses dispensed at different times between 2017 to 2019, but none were related to the change of procurement procedures. At all levels of care, the COVID-19 onset in April 2020 caused an abrupt drop in doses dispensed especially at the primary level. Regarding availability, we found an increasing linear trend in the primary level of care for both medicines. In secondary and tertiary levels of care, the availability fluctuated between 40 and 95%. The onset of the COVID-19 pandemic did not significantly impact medicine availability, except for losartan in the tertiary level of care. Conclusion The availability and proper dispensing of first-line medicines for hypertension and diabetes is an essential factor for sustainable and equitable treatment. Health care systems need to be prepared for forecasting the increasing demand of medicines for chronic diseases, but also to maintain effective medicine supply chains during humanitarian crisis like pandemics.

Varicella is a contagious disease of children. Qingdao administrated free one-dose and free two-dose universal varicella vaccination schedules in 2013 and 2016 for preschool children. The effectiveness of the vaccination was analyzed in this study. Monthly varicella incidence data of 1–6 years old children during 2007–2020 were obtained from the Qingdao Infectious Disease Reporting Information Management System. We applied Interrupted time series and segmented regression analyses to assess changes in varicella incidence at the beginning of each month and average monthly changes during the vaccination. The vaccination was associated with a reduction of 32.7% in varicella morbidity on average during the 8-year intervention, there is a statistically significant difference between the voluntary period and free vaccination period (χ2 = 290.80,P

Objective: Social distancing has been confirmed to reduce the incidence of not only the COVID-19, but also the incidence of other diseases. Therefore, this study aimed to investigate the effect of social distancing policies on the incidence of infectious eye diseases by monitoring their nationwide incidence data in all age groups.Methods: In this study, to analyse the impact of COVID-19 policy on IEDSC, the time periods were divided into two interventions. The first intervention was the first COVID-19 patient report in Korea on 19 January 2020. The second intervention was relaxation of the social distancing policy on 6 May 2020. Segmented regression analysis of the interrupted time series was used to assess COVID-19 policies on the IEDSC.Results: After the first incidence of a COVID-19 patient, IEDSCs decreased significantly in all age groups, while the relaxation of the social distancing policy increased IEDSCs significantly, mostly in all groups.Conclusion: In the post-COVID-19 era, we hope that national-level interventions such as reducing air pollution and employing precautionary measures will significantly reduce the financial burden of developing infectious ophthalmic diseases.

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be "interrupted" by a change in a particular method of health care delivery. Interrupted time series (ITS) is a robust quasi-experimental design with the ability to infer the effectiveness of an intervention that accounts for data dependency. Current standardized methods for analyzing ITS data do not model changes in variation and correlation following the intervention. This is a key limitation since it is plausible for data variability and dependency to change because of the intervention. Moreover, present methodology either assumes a prespecified interruption time point with an instantaneous effect or removes data for which the effect of intervention is not fully realized. In this paper, we describe and develop a novel robust interrupted time series (robust-ITS) model that overcomes these omissions and limitations. The robust-ITS model formally performs inference on (1) identifying the change point; (2) differences in preintervention and postintervention correlation; (3) differences in the outcome variance preintervention and postintervention; and (4) differences in the mean preintervention and postintervention. We illustrate the proposed method by analyzing patient satisfaction data from a hospital that implemented and evaluated a new nursing care delivery model as the intervention of interest. The robust-ITS model is implemented in an R Shiny toolbox, which is freely available to the community.

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag–York–Mollié model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%. [ABSTRACT FROM AUTHOR]

Background: A classic methodology used in evaluating the impact of health policy interventions is interrupted time-series (ITS) analysis, applying a quasi-experimental design that uses both pre- and post-policy data without randomization. In this paper, we took a simulation-based approach to estimating intervention effects under different assumptions.Methods: Each of the simulated mortality rates contained a linear time trend, seasonality, autoregressive, and moving-average terms. The simulations of the policy effects involved three scenarios: 1) immediate-level change only, 2) immediate-level and slope change, and 3) lagged-level and slope change. The estimated effects and biases of these effects were examined via three matched generalized additive mixed models, each of which used two different approaches: 1) effects based on estimated coefficients (estimated approach), and 2) effects based on predictions from models (predicted approach). The robustness of these two approaches was further investigated assuming misspecification of the models.Results: When one simulated dataset was analyzed with the matched model, the two analytical approaches produced similar estimates. However, when the models were misspecified, the number of deaths prevented, estimated using the predicted vs. estimated approaches, were very different, with the predicted approach yielding estimates closer to the real effect. The discrepancy was larger when the policy was applied early in the time-series.Conclusion: Even when the sample size appears to be large enough, one should still be cautious when conducting ITS analyses, since the power also depends on when in the series the intervention occurs. In addition, the intervention lagged effect needs to be fully considered at the study design stage (i.e., when developing the models). [ABSTRACT FROM AUTHOR]

Since its release of Version 1.0 in 1998, Joinpoint software developed for cancer trend analysis by a team at the US National Cancer Institute has received a considerable attention in the trend analysis community and it became one of most widely used software for trend analysis. The paper published in Statistics in Medicine in 2000 (a previous study) describes the permutation test procedure to select the number of joinpoints, and Joinpoint Version 1.0 implemented the permutation procedure as the default model selection method and employed parametric methods for the asymptotic inference of the model parameters. Since then, various updates and extensions have been made in Joinpoint software. In this paper, we review basic features of Joinpoint, summarize important updates of Joinpoint software since its first release in 1998, and provide more information on two major enhancements. More specifically, these enhancements overcome prior limitations in both the accuracy and computational efficiency of previously used methods. The enhancements include: (i) data driven model selection methods which are generally more accurate under a broad range of data settings and more computationally efficient than the permutation test and (ii) the use of the empirical quantile method for construction of confidence intervals for the slope parameters and the location of the joinpoints, which generally provides more accurate coverage than the prior parametric methods used. We show the impact of these changes in cancer trend analysis published by the US National Cancer Institute. [ABSTRACT FROM AUTHOR]

The objective of the present work was to determine the age of transition from juvenile to mature wood from the study of the axial and radial variation of density and to carry out descriptive models of distribution of juvenile and mature wood in the stem of Populus deltoides, clone 'Stoneville 67' of 20 years of age. The density presented an increase in its values from the medulla to the cortex and from the base to the apex. The transition age was determined between 10-12 years of age, the volume of juvenile and mature wood was 40% and 60%, respectively. The distribution of juvenile wood coincided with the central cylinder model. [ABSTRACT FROM AUTHOR]