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.
OBJECTIVE: 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.

UNASSIGNED: Segmented regression, a common model for interrupted time series (ITS) analysis, primarily utilizes two equation parametrizations. Interpretations of coefficients vary between the two segmented regression parametrizations, leading to occasional user misinterpretations.
UNASSIGNED: To illustrate differences in coefficient interpretation between two common parametrizations of segmented regression in ITS analysis, we derived analytical results and present an illustration evaluating the impact of a smoking regulation policy in Italy using a publicly accessible dataset. Estimated coefficients and their standard errors were obtained using two commonly used parametrizations for segmented regression with continuous outcomes. We clarified coefficient interpretations and intervention effect calculations.
UNASSIGNED: Our investigation revealed that both parametrizations represent the same model. However, due to differences in parametrization, the immediate effect of the intervention is estimated differently under the two approaches. The key difference lies in the interpretation of the coefficient related to the binary indicator for intervention implementation, impacting the calculation of the immediate effect.
UNASSIGNED: Two common parametrizations of segmented regression represent the same model but have different interpretations of a key coefficient. Researchers employing either parametrization should exercise caution when interpreting coefficients and calculating intervention effects.

BACKGROUND: 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.
CONCLUSIONS: 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.

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.

UNASSIGNED: 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.
UNASSIGNED: We conducted an interrupted time series analysis to examine rates of cataract surgery before and during the lockdown.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

OBJECTIVE: Smoke-free areas have expanded and related campaigns have been implemented since 1995 in Korea. As a result, household secondhand smoke (SHS) exposure has decreased over the past 15 years. We assessed the cohort effect, the effect of a 2008 campaign on household SHS exposure, and the impact of a complete smoking ban in public places along with increased penalties, as implemented in December 2011.
METHODS: Nationally representative cross-sectional 15-wave survey data of Korean adolescents were used. The 810,516 participants were classified into 6 grade groups, 15 period groups, and 20 middle school admission cohorts. An age-period-cohort analysis, conducted with the intrinsic estimator method, was used to assess the cohort effect of household SHS exposure, and interrupted-time series analyses were conducted to evaluate the effects of the smoke-free policy and the campaign.
RESULTS: For cohorts who entered middle school from 2002 to 2008, the risk of household SHS exposure decreased among both boys and girls. Immediately after implementation of the smoke-free policy, the prevalence of household SHS exposure by period decreased significantly for boys (coefficient, -8.96; p<0.05) and non-significantly for girls (coefficient, -6.99; p=0.07). After the campaign, there was a significant decrease in household SHS exposure by cohort among boys, both immediately and post-intervention (coefficient, -4.84; p=0.03; coefficient, -1.22; p=0.02, respectively).
CONCLUSIONS: A school-admission-cohort effect was found on household SHS exposure among adolescents, which was associated with the smoke-free policy and the campaign. Anti-smoking interventions should be implemented consistently and simultaneously.

Two-phase polynomial regression models (Robison, 1964; Fuller, 1969; Gallant and Fuller, 1973; Zhan et al., 1996) are widely used in ecology, public health, and other applied fields to model nonlinear relationships. These models are characterized by the presence of threshold parameters, across which the mean functions are allowed to change. That the threshold is a parameter of the model to be estimated from the data is an essential feature of two-phase models. It distinguishes them, and more generally, multi-phase models, from the spline models and has profound implications for both computation and inference for the models. Estimation of two-phase polynomial regression models is a non-convex, non-smooth optimization problem. Grid search provides high quality solutions to the estimation problem, but is very slow when done by brute force. Building upon our previous work on piecewise linear two-phase regression models estimation, we develop fast grid search algorithms for two-phase polynomial regression models and demonstrate their performance. Furthermore, we develop bootstrap-based pointwise and simultaneous confidence bands for mean functions. Monte Carlo studies are conducted to demonstrate the computational and statistical properties of the proposed methods. Three real datasets are used to help illustrate the application of two-phase models, with special attention on model choice.

Hepatitis 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.
The 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.
The 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.
Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

UNASSIGNED: The Saudi Food and Drug Authority (SFDA) classified pregabalin as a controlled substance in 2018; however, whether this policy change has affected pregabalin use is unclear. This study examined the trends in pregabalin prescriptions before and after the SFDA restriction. In addition, the co-prescription of controlled analgesics and the use of pregabalin for approved indications were also evaluated.
UNASSIGNED: A cross-sectional study was conducted on outpatient pregabalin prescriptions from three healthcare centers in Saudi Arabia. Interrupted time series analysis was used to assess changes over time in pregabalin prescriptions and the number of patients receiving pregabalin. June 2016 to June 2017 was identified as the pre-restriction period, and July 2018 to July 2019 as the post-restriction period.
UNASSIGNED: In this study, 77,760 pregabalin prescriptions were identified. There were 9,076 patients on pregabalin in the pre-restriction period with 16,875 prescriptions, compared with 7,123 patients and 19,484 prescriptions post-restriction. The total number of pregabalin users decreased by 21.5% post-restriction, and prescriptions increased by 15.5%. There was no significant change in the monthly trends in pregabalin prescriptions before and after the restriction. However, the of tramadol and acetaminophen/codeine prescriptions in patients who were using pregabalin increased in the post-restriction period by 21% and 16.1%, respectively.
UNASSIGNED: Pregabalin use was reduced after the SFDA-enforced prescription restriction was implemented. This was accompanied by increased narcotics use in the post-implementation phase.

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.