Baseline kidney function following kidney transplantation is often used in research and clinical decision-making yet is not well defined. Here, a method to determine baseline function was proposed and validated on three single-center retrospective cohorts consisting of 922 patients from Belgium (main cohort) and two validation cohorts of 987 patients from the Netherlands and 519 patients from Germany. For each transplant, a segmented regression model was fitted on the estimated glomerular filtration rate (eGFR) evolution during the first-year post-transplantation. This yielded estimates for change point timing, rate of eGFR change before and after change point and eGFR value at change point, now considered the \"baseline function\". Associations of eGFR evolution with recipient/donor characteristics and the graft failure rate were assessed with linear regression and Cox regression respectively. The change point occurred on average at an eGFR value of 43.7±14.6 mL/min/1.73m2, at a median time of 6.5 days post-transplantation. Despite significant associations with several baseline donor-recipient characteristics (particularly, donor type; living vs deceased), the predictive value of these characteristics for eGFR value and timing of the change point was limited. This followed from a large heterogeneity within eGFR trajectories, which in turn indicated that favorable levels of kidney function could be reached despite a suboptimal initial evolution. Segmented regression consistently provided a good fit to early eGFR evolution, and its estimate of the change point can be a useful reference value in future analyses. Thus, our study shows that baseline kidney function after transplantation is heterogeneous and partly related to pretransplant donor characteristics.

UNASSIGNED: Inappropriate use of antibiotics is expected to increase during the COVID-19 pandemic, but there are limited data on COVID-19\'s long-term impact. We assessed the impact of COVID-19 on the quantity and quality of antibiotic use in Scotland.
UNASSIGNED: A segmented interrupted time series was applied to monthly dispensed antibiotics using prescription cost analysis data from March/2019 to March/2023. Antibiotic use was quantified using the number of items dispensed/1000 inhabitants (TIDs) and defined daily dose/1000 inhabitants/day (DIDs). The quality of antibiotic use was assessed using key quality indicators including the WHO AWaRe classification, proportion of broad-spectrum and \"4C\"-antibiotics.
UNASSIGNED: Overall, for all antibiotics, there was a non-significant increase in TIDs and DIDs before the first lockdown (March/2020) (β1), but a decline in the level immediately after the first (β2) and second lockdowns (β4) (November/2020), albeit non-significant. However, a significant increase in the time trend after the second lockdown (β5) for all antibiotic classes was observed. COVID-19 had no negative impact on AWaRe utilisation, with the proportion of all antibiotics from the Access group increasing from 76% in March/2019 to 90% in March/2023. The proportion of \"4C\" antibiotic reduced significantly after the second lockdown.
UNASSIGNED: Neither the utilisation nor the quality of total antibiotic use appeared to have been significantly affected by COVID-19.

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.

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.

There is evidence suggesting that COVID-19 vaccination may be associated with small, transitory effects on uterine bleeding, possibly including menstrual timing, flow, and duration, in some individuals. However, changes in health care seeking, diagnosis, and workup for abnormal uterine bleeding in the COVID-19 vaccine era are less clear.
This study aimed to assess the impact of COVID-19 vaccination on incident abnormal uterine bleeding diagnosis and diagnostic evaluation in a large integrated health system.
Using segmented regression, we assessed whether the availability of COVID-19 vaccines was associated with changes in monthly, population-based rates of incident abnormal uterine bleeding diagnoses relative to the prepandemic period in health system members aged 16 to 44 years who were not menopausal. We also compared clinical and demographic characteristics of patients diagnosed with incident abnormal uterine bleeding between December 2020 and October 13, 2021 by vaccination status (never vaccinated, vaccinated in the 60 days before diagnosis, vaccinated >60 days before diagnosis). Furthermore, we conducted detailed chart review of patients diagnosed with abnormal uterine bleeding within 1 to 60 days of COVID-19 vaccination in the same time period.
In monthly populations ranging from 79,000 to 85,000 female health system members, incidence of abnormal uterine bleeding diagnosis per 100,000 person-days ranged from 8.97 to 19.19. There was no significant change in the level or trend in the incidence of abnormal uterine bleeding diagnoses between the prepandemic (January 2019-January 2020) and post-COVID-19 vaccine (December 2020-December 2021) periods. A comparison of clinical characteristics of 2717 abnormal uterine bleeding cases by vaccination status suggested that abnormal bleeding among recently vaccinated patients was similar or less severe than abnormal bleeding among patients who had never been vaccinated or those vaccinated >60 days before. There were also significant differences in age and race of patients with incident abnormal uterine bleeding diagnoses by vaccination status (Ps<.02). Never-vaccinated patients were the youngest and those vaccinated >60 days before were the oldest. The proportion of patients who were Black/African American was highest among never-vaccinated patients, and the proportion of Asian patients was higher among vaccinated patients. Chart review of 114 confirmed postvaccination abnormal uterine bleeding cases diagnosed from December 2020 through October 13, 2021 found that the most common symptoms reported were changes in timing, duration, and volume of bleeding. Approximately one-third of cases received no diagnostic workup; 57% had no etiology for the bleeding documented in the electronic health record. In 12% of cases, the patient mentioned or asked about a possible link between their bleeding and their recent COVID-19 vaccine.
The availability of COVID-19 vaccination was not associated with a change in incidence of medically attended abnormal uterine bleeding in our population of over 79,000 female patients of reproductive age. In addition, among 2717 patients with abnormal uterine bleeding diagnoses in the period following COVID-19 vaccine availability, receipt of the vaccine was not associated with greater bleeding severity.

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.