Abnormal uterine bleeding diagnoses and care following COVID-19 vaccination

BACKGROUND

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.

OBJECTIVE

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.

STUDY DESIGN

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.

RESULTS

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.

CONCLUSION

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.

Reproductive traits in Pura Raza Española mares manifest inbreeding depression from low levels of homozygosity

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.

Postmenopausal bleeding after COVID-19 vaccination

BACKGROUND

There is a growing literature base regarding menstrual changes following COVID-19 vaccination among premenopausal people. However, relatively little is known about uterine bleeding in postmenopausal people following COVID-19 vaccination.

OBJECTIVE

This study aimed to examine trends in incident postmenopausal bleeding diagnoses over time before and after COVID-19 vaccine introduction, and to describe cases of new-onset postmenopausal bleeding after COVID-19 vaccination.

STUDY DESIGN

For postmenopausal bleeding incidence calculations, monthly population-level cohorts consisted of female Kaiser Permanente Northwest members aged ≥45 years. Those diagnosed with incident postmenopausal bleeding in the electronic medical record were included in monthly numerators. Members with preexisting postmenopausal bleeding or abnormal uterine bleeding, or who were at increased risk of bleeding due to other health conditions, were excluded from monthly calculations. We used segmented regression analysis to estimate changes in the incidence of postmenopausal bleeding diagnoses from 2018 through 2021 in Kaiser Permanente Northwest members meeting the inclusion criteria, stratified by COVID-19 vaccination status in 2021. In addition, we identified all members with ≥1 COVID-19 vaccination between December 14, 2020 and August 14, 2021, who had an incident postmenopausal bleeding diagnosis within 60 days of vaccination. COVID-19 vaccination, diagnostic procedures, and presumed bleeding etiology were assessed through chart review and described. A temporal scan statistic was run on all cases without clear bleeding etiology.

RESULTS

In a population of 75,530 to 82,693 individuals per month, there was no statistically significant difference in the rate of incident postmenopausal bleeding diagnoses before and after COVID-19 vaccine introduction (P=.59). A total of 104 individuals had incident postmenopausal bleeding diagnosed within 60 days following COVID-19 vaccination; 76% of cases (79/104) were confirmed as postvaccination postmenopausal bleeding after chart review. Median time from vaccination to bleeding onset was 21 days (range: 2-54 days). Among the 56 postmenopausal bleeding cases with a provider-attributed etiology, the common causes of bleeding were uterine or cervical lesions (50% [28/56]), hormone replacement therapy (13% [7/56]), and proliferative endometrium (13% [7/56]). Among the 23 cases without a clear etiology, there was no statistically significant clustering of postmenopausal bleeding onset following vaccination.

CONCLUSION

Within this integrated health system, introduction of COVID-19 vaccines was not associated with an increase in incident postmenopausal bleeding diagnoses. Diagnosis of postmenopausal bleeding in the 60 days following receipt of a COVID-19 vaccination was rare.

Injury, Musculoskeletal Symptoms, and Stress as a Function of Aging in Agricultural Operators in the Central United States

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.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021

Objective

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.

Method

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.

Results

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.

Conclusion

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

Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study

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.

Evaluating the Effect of COVID-19 on Outpatient Opioid Utilization Among Health First Colorado Members and a National Non-Medicaid Cohort: An Interrupted Time Series Analysis

Objective

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.

Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments

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.

Post Antenatal Late Preterm Steroids trial: interrupted time series analysis of respiratory outcomes in twin and pregestational diabetes

BACKGROUND

The Antenatal Late Preterm Steroids trial found that corticosteroid administration decreased respiratory complications by 20% among late preterm singleton deliveries. After the Antenatal Late Preterm Steroids trial, corticosteroid administration increased by 76% among twin pregnancies and 113% among singleton pregnancies complicated by pregestational diabetes mellitus compared with expected rates based on the pre-Antenatal Late Preterm Steroids trial trend. However, the effect of corticosteroids on twin pregnancies and pregnancies complicated by pregestational diabetes mellitus is not well studied, as the Antenatal Late Preterm Steroids trial excluded twin pregnancies and pregnancies complicated by pregestational diabetes mellitus.

OBJECTIVE

This study aimed to examine the change in the incidence rate of immediate assisted ventilation use and ventilation use for more than 6 hours among 2 populations after the dissemination of the Antenatal Late Preterm Steroids trial at the population level.

STUDY DESIGN

This study was a retrospective analysis of publicly available US birth certificate data. The study period was from August 1, 2014, to April 30, 2018. The dissemination period of the Antenatal Late Preterm Steroids trial was from February 2016 to October 2016. Population-based interrupted time series analyses were performed for 2 target populations: (1) twin pregnancies not complicated by pregestational diabetes mellitus and (2) singleton pregnancies complicated by pregestational diabetes mellitus. For both target populations, analyses were limited to individuals who delivered nonanomalous live neonates between 34 0/7 and 36 6/7 weeks of gestation (vaginal or cesarean delivery). As a sensitivity analysis, a total of 23 placebo tests were conducted before (5 tests) and after (18 tests) the dissemination period.

RESULTS

For the analysis of late preterm twin deliveries, 191,374 individuals without pregestational diabetes mellitus were identified. For the analysis of late preterm singleton pregnancy with pregestational diabetes mellitus, 21,395 individuals were identified. After the dissemination period, the incidence rate of immediate assisted ventilation use for late preterm twin deliveries was significantly lower than the expected value based on the pre-Antenatal Late Preterm Steroids trial trend (11.6% observed vs 13.0% expected; adjusted incidence rate ratio, 0.87; 95% confidence interval, 0.78-0.97). The incidence rate of ventilation use for more than 6 hours among late preterm twin deliveries did not change significantly after the dissemination of the Antenatal Late Preterm Steroids trial. A significant increase in the incidence rate of immediate assisted ventilation use and ventilation use for more than 6 hours was found among singleton pregnancies with pregestational diabetes mellitus. However, the results of placebo tests suggested that the increase in incidence was not necessarily due to the dissemination period of the Antenatal Late Preterm Steroids trial.

CONCLUSION

The dissemination of the Antenatal Late Preterm Steroids trial was associated with decreased incidence of immediate assisted ventilation use, but no change in ventilation use for more than 6 hours, among late preterm twin deliveries in the United States. In contrast, the incidence of neonatal respiratory outcomes among singleton deliveries with pregestational diabetes mellitus did not decrease after the dissemination of the Antenatal Late Preterm Steroids trial.

Investigating the Marginal and Herd Effects of COVID-19 Vaccination for Reducing Case Fatality Rate: Evidence from the United States between March 2021 to January 2022

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.

Association Between Accreditation Era, North American Pharmacist Licensure Examination Testing Changes, and First-Time Pass Rates

Objective. To estimate whether first-time pass rates on the North American Pharmacist Licensure Examination (NAPLEX) have been influenced by the number of pharmacy programs founded since 2000, the programs' accreditation era, and the changes to the blueprint as well as changes to the testing conditions and passing standards implemented by the National Association of Boards of Pharmacy (NABP) beginning in 2015.Methods. This was a retrospective, observational cohort study using publicly published data. The number of programs and pass rates were collected from 2008 to 2020. Programs reporting pass rates from 2016 to 2020 were eligible. Accreditation era was defined as programs accredited before or after 2000. Pass rates were categorized into NAPLEX tests administered before or after 2015. Statistical analyses were conducted for comparisons.Results. Pass rates were initially found to decline as the number of programs rose. First-time pass rates of programs accredited before 2000 were higher than pass rates of programs accredited after 2000 every year after 2011. Only 40% of the programs accredited after 2000 exceeded the national average between 2016-2020. Blueprint changes implemented in 2015 and the changes to testing conditions plus passing standards implemented in 2016 had a greater effect on pass rates than the number of programs or applicants.Conclusion. Programs accredited after 2000 generally had lower first-time NAPLEX pass rates. Even so, blueprint changes and changes to the testing conditions plus passing standards instituted by the NABP were more important predictors of the decline of first-time NAPLEX pass rates. Stakeholders should collaborate and embrace best practices for assessing practice-ready competency for licensure.

A new analytic tool for assessing the impact of the US Food and Drug Administration regulatory actions

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.

Using Joinpoint regression for drug utilization research: Tutorial and case study of prescription opioid use in the United States

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.

Range reexpansion after long stasis: Italian otters (Lutra lutra) at their northern edge

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.

Evaluating the impact of universal varicella vaccination among preschool-aged children in Qingdao, China: An interrupted time-series analysis

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 < 0.001). Immediately after the free one-dose vaccination implementation in 2013 and free two-dose vaccination implementation in 2016, varicella incidence decreased by 0.135 cases per 100 000 population (P < 0.001) and increased by 1.189 cases per 100 000 population (P = 0.039), respectively, the results were statistically significant. There were significant declining trends in varicella incidence after free vaccination: 0.135(P < 0.001) and 0.055 (P = 0.025) per month in 2013.7-2016.6 and 2016.7-2020.12, respectively. This study shows a further decaying trend of varicella incidence based on the impact of free two-dose vaccination. It is necessary to prolong free two-dose universal varicella vaccination to strengthen the immune barrier of preschool children sequentially.

Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.

Twenty years since Joinpoint 1.0: Two major enhancements, their justification, and impact

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.

Effects of public health measures during the SARS-CoV-2 pandemic on the winter respiratory syncytial virus epidemic: An interrupted time series analysis

BACKGROUND

Public health measures (PHM) designed to contain the spread of COVID-19 pandemic have influenced the epidemiological characteristics of other viral infections. Its impact on acute RSV bronchiolitis in infants of ≤24 months old has not been systematically studied in our setting.

OBJECTIVES

To describe the monthly pattern of visits to the Paediatric Emergency Department (PED) of patients 0 to 14 years of age, the rate of patients diagnosed with RSV acute bronchiolitis per thousand inhabitants of 0 to 24 months, and the rate of them requiring hospital admission during the winter 2020-2021, in the context of local and national COVID-19 restrictions and compare them to the four previous seasons.

METHODS

Interrupted time series analysis of patients assisted in the PED and diagnosed with or admitted for RSV acute bronchiolitis in a tertiary University Hospital from January 2016 to February 2020 (pre-intervention period) and from March 2020 to June 2021 (post-intervention period).

INTERVENTION

Preventive PHM implemented by the Spanish government weighted by the Containment and Health Index of the Oxford COVID-19 Government Response Tracker.

RESULTS

The intervention was followed by an immediate reduction of the rate of visits to the PED of -19.5 (95% confidence interval [CI] -24.0, -14.9) per thousand, and the rate of diagnoses and admissions for RSV acute bronchiolitis of -44.3 (95% CI -73.8, -14.8) and -1.4 (95% CI -2.7, -0.1) per thousand, respectively, with a delayed rebound.

CONCLUSIONS

After the implementation of PHM to prevent the spread of SARS-CoV-2 infection, an immediate and important decline in the visits to the PED was observed, with an upward change thereafter. There was also an initial reduction in the diagnoses of and admissions by RSV acute bronchiolitis. An upward trend was observed six to nine months after the usual time of the winter RSV epidemic, coinciding with the relaxation of the preventive PHM.

The Impact of Regulatory Restrictions on Pregabalin use in Saudi Arabia: An Interrupted Time series Analysis

PURPOSE

The Saudi Food and Drug Authority (SFDA) added pregabalin to the list of controlled substances in December 2017 to minimize the risk of its possible abuse and misuse. This study was aimed at assessing the impact of this decision on the overall use of pregabalin in Saudi Arabia and in comparison, with drugs prescribed to treat neuropathic pain therapy (i.e. vs. gabapentin, tramadol, duloxetine, and amitriptyline).

METHODS

This was an interrupted time-series analysis of the Saudi quarterly sale data of the study drugs from October/2015 to September/2020. These data were obtained from IQVIA and were converted into use estimates (defined daily dose per 1000 inhabitant-days [DDD/TID]). Segmented regression models were conducted to assess the direct (level) and prolonged (trend) changes in use data after the decision. All analyses were completed using RStudio Version 1.4.1103.

RESULTS

Before the SFDA's decision, there was an increased quarter-to-quarter use of pregabalin (DDD/TID: 0.16; 95% confidence interval [CI] 0.04 to 0.28). Pregabalin overall use dropped sharply by -1.85 DDD/TID (95%CI -2.71 to -0.99) directly after the decision with a prolonged quarter-to-quarter declining effect (DDD/TID: -0.22, CI to -0.37 to -0.05). The decision was associated with a direct increase in the use of gabapentin by 0.62 DDD/TID (95%CI 0.52 to 0.72) without any impact on the use of other drugs.

CONCLUSIONS

The results of our study showed that the SFDA decision was associated with a decrease in the overall use of pregabalin, which may help minimize the risk of its abuse and misuse. This article is protected by copyright. All rights reserved.

Methodological systematic review recommends improvements to conduct and reporting when meta-analysing interrupted time series studies

OBJECTIVES

Interrupted Time Series (ITS) are a type of non-randomised design commonly used to evaluate public health policy interventions, and the impact of exposures, at the population level. Meta-analysis may be used to combine results from ITS across studies (in the context of systematic reviews) or across sites within the same study. We aimed to examine the statistical approaches, methods, and completeness of reporting in reviews that meta-analyse results from ITS.

STUDY DESIGN AND SETTINGS

Eight electronic databases were searched to identify reviews (published 2000-2019) that meta-analysed at least two ITS. Characteristics of the included reviews, the statistical methods used to analyse the ITS and meta-analyse their results, effect measures, and risk of bias assessment tools were extracted.

RESULTS

Of the 4213 identified records, 54 reviews were included. Nearly all reviews (94%) used two-stage meta-analysis, most commonly fitting a random effects model (69%). Among the 41 reviews that re-analysed the ITS, linear regression (39%) and ARIMA (20%) were most commonly used; 38% adjusted for autocorrelation. The most common effect measure meta-analysed was an immediate level-change (46/54). Reporting of the statistical methods and ITS characteristics was often incomplete.

CONCLUSION

Improvement is needed in the conduct and reporting of reviews that meta-analyse results from ITS.

Ambulatory Fluoroquinolone Use in the United States, 2015-2019

Background

Frequently used fluoroquinolones have been subject to increasing safety concerns and regulatory alerts. This study characterized ambulatory fluoroquinolone utilization in the United States and evaluated the impact of 2016 Food and Drug Administration (FDA) safety advisories on its use.

Methods

We used IQVIA's National Disease and Therapeutic Index to quantify adult outpatient fluoroquinolone use ("treatment visits"). Descriptive statistics and segmented regression were used to report trends and quantify the varied use before and after FDA's 2016 alerts.

Results

Between 2015 to 2019, fluoroquinolone use decreased by 26.7% (18.7 million treatment visits in 2015 to 13.7 million treatment visits in 2019). Annual use declined by 44%, 24%, and 24% for respiratory, urogenital, and gastrointestinal conditions, respectively; and by 66% among providers ≤44 years old vs negligible decline among those ≥65 years old. Before 2016 FDA advisories, there were approximately 4.8 million fluoroquinolone treatment visits/quarter, which had a statistically significant immediate drop by 641035 visits (95% confidence interval [CI], -937368 to -344702; P=.000) after FDA's 2016 advisories. A statistically significant difference of approximately 45000 visits/quarter (95% CI, -85956 to -3122; P=.036) was observed after the advisories.

Conclusions

Large reductions in ambulatory fluoroquinolone use in the United States have coincided with increasing evidence of safety concerns and FDA advisories. However, fluoroquinolone use varies significantly based on patient and provider characteristics, suggesting heterogeneous effects of emerging risks on clinical practice.

Healthcare Quality Improvement Analytics: An Example Using Computerized Provider Order Entry

Evaluation of sustainability after quality improvement (QI) projects in healthcare settings is an essential part of monitoring and future QI planning. With limitations in adopting quasi-experimental study design in real-world practice, healthcare professionals find it challenging to present the sustained effect of QI changes effectively. To provide quantitative methodological approaches for demonstrating the sustainability of QI projects for healthcare professionals, we conducted data analyses based on a QI project to improve the computerized provider order entry system to reduce patients' dosing frequencies in Korea. Data were collected for 5 years: 24-month pre-intervention, 12-month intervention, and 24-month post-intervention. Then, analytic approaches including control chart, Analysis of Variance (ANOVA), and segmented regression were performed. The control chart intuitively displayed how the outcomes changed over the entire period, and ANOVA was used to test whether the outcomes differed between groups. Last, segmented regression analysis was conducted to evaluate longitudinal effects of interventions over time. We found that the impact of QI projects in healthcare settings should be initiated following the Plan-Do-Study-Act cycle and evaluated long-term effects while widening the scope of QI evaluation with sustainability. This study can serve as a guide for healthcare professionals to use a number of statistical methodologies in their QI evaluations.

Effects of Regulation on Carbapenem Prescription in a Large Teaching Hospital in China: An Interrupted Time Series Analysis, 2016-2018

Purpose

Carbapenem resistance due to the overuse of carbapenems has become a public health problem worldwide, particularly in low- and middle-income countries (LMICs). However, there are few policies guiding carbapenem prescription, and their effectiveness is still unclear. A regulation targeting carbapenem prescription was implemented in March 2017 in China. This study aimed to assess the effects of the regulation for providing evidence on the prudent use of carbapenems.

Patients and Methods

This was an interventional, retrospective study started in January 2017. The intervention covered establishing performance appraisal indicators, special authorisation, strict prescribing restrictions, and dedicated supervision, particularly in the intensive care unit (ICU). Data on adult inpatients who received at least one carbapenems were extracted from January 2016 to December 2018. Segmented regression analysis was performed to evaluate the effect of the regulation.

Results

A total of 2005 inpatients received carbapenems. Segmented regression models showed an immediate decline in the intensity of antibiotic consumption (IAC) of carbapenems (coefficient = −9.65, p < 0.001), particularly imipenem (coefficient = −6.82, p = 0.002), and the antibiotic consumption of carbapenems (coefficient = −133.60, p = 0.003) in the ICU. And there is a decreasing trend in the IAC of meropenem (coefficient = −0.03, p = 0.008) in all departments. Furthermore, the IAC of carbapenems and imipenem (coefficient = −0.36, p = 0.035; coefficient = −0.49, p = 0.025, respectively), and the average length of stay (ALoS) (coefficient = −0.73, p < 0.001) showed downward trends in the ICU.

Conclusion

The intervention effectively reduced the IAC of carbapenems and imipenem, carbapenem consumption and the ALoS in the ICU, and the IAC of meropenem in all departments. The effects of the intervention were significant in the ICU, which indicated an urgent need for stronger regulations focusing on critical departments in the future.

Reduction in antimicrobial use associated with a multifaceted antimicrobial stewardship programme in a tertiary teaching hospital in Shanghai: a segmented regression analysis

BACKGROUND

Rational use of antibiotics received great attention in China, therefore the multifaceted antimicrobial stewardship (MAMS) is urgently required in hospital management. We conducted this study to assess the impact of a MAMS programme on antimicrobial use in a tertiary teaching hospital in Shanghai.

METHODS

This retrospective observational study was conducted at a tertiary teaching hospital in Shanghai. The MAMS programme involved multifaceted interventions consisting of a quality premium with financial incentives, antibiotic restriction, audit and feedback, and education. Data were extracted from the electronic medical records of inpatients to analyse monthly and annual antibiotic consumption and the percentage of antibiotic prescriptions during 2017-2020. Segmented regression analysis of the interrupted time series was used to contrast antimicrobial use during 2019-2020, with non-MAMS data from the 2017-2018 period as the historical control.

RESULTS

With MAMS implementation, antibiotic consumption decreased from 63.3 (59.3, 67.2) defined daily doses (DDDs) per 100 patient-days (PD) to 43.3 (39.0, 49.8) DDDs/100 PD (P<0.001), and the percentage of antibiotic prescriptions decreased from 44.8% (44.1%, 45.4%) to 43.3% (42.2%, 44.3%) (P<0.001). Segmented regression models suggested a reduction in antibiotic consumption (coefficient = -12.537, P<0.001) and indicated a downward trend in the percentage of antibiotic prescriptions (coefficient =-0.165, P=0.049). Neither antibiotic consumption nor the percentage of antibiotic prescriptions was influenced by the coronavirus disease 2019 (COVID-19) pandemic.

CONCLUSIONS

This study suggests that MAMS plays an important role in reducing antibiotic use and is not affected by special circumstances such as the COVID-19 pandemic. This novel intervention, consisting of a quality premium and multidisciplinary cooperation, should be prioritized by policy and decision makers, where rational management of antimicrobial use is urgently needed.

Modelling and Differential Quantification of Electric Cell-Substrate Impedance Sensing Growth Curves

Measurement of cell surface coverage has become a common technique for the assessment of growth behavior of cells. As an indirect measurement method, this can be accomplished by monitoring changes in electrode impedance, which constitutes the basis of electric cell-substrate impedance sensing (ECIS). ECIS typically yields growth curves where impedance is plotted against time, and changes in single cell growth behavior or cell proliferation can be displayed without significantly impacting cell physiology. To provide better comparability of ECIS curves in different experimental settings, we developed a large toolset of R scripts for their transformation and quantification. They allow importing growth curves generated by ECIS systems, edit, transform, graph and analyze them while delivering quantitative data extracted from reference points on the curve. Quantification is implemented through three different curve fit algorithms (smoothing spline, logistic model, segmented regression). From the obtained models, curve reference points such as the first derivative maximum, segmentation knots and area under the curve are then extracted. The scripts were tested for general applicability in real-life cell culture experiments on partly anonymized cell lines, a calibration setup with a cell dilution series of impedance versus seeded cell number and finally IPEC-J2 cells treated with 1% and 5% ethanol.

Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research

Objective

Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they were evaluated, reported and handled.

Study Design and Setting

This was a scoping review following standard recommendations from the PRISMA Extension for Scoping Reviews. We included a random sample of all ITS studies that assessed any intervention relevant to health care (eg, policies or programmes) with individual-level data, published in 2019, with abstracts indexed on MEDLINE.

Results

From 732 studies identified, we finally reviewed 60. Reporting of missing data was rare. Data aggregation, statistical tools for modelling population-level data and complete case analyses were preferred, but these can lead to bias when data are missing at random. Seasonality and other time-dependent confounders were rarely accounted for and, when they were, missing data implications were typically ignored. Very few studies reflected on the consequences of missing data.

Conclusion

Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best practice.

Timing and temperature thresholds of heat stress effects on fertility performance of different parity sows in Spanish herds

High temperature is an environmental factor that impairs sow fertility. In this study, we identified the critical weeks for heat stress effects on aspects of fertility performance, namely weaning-to-first-service interval (WSI) and farrowing rate (FR). We also examined the threshold temperatures above which the fertility performance deteriorated and whether there were any differences between parities regarding heat stress effects or thresholds. Performance data of sows in 142 herds from 2011 to 2016 were matched to appropriate weekly averaged daily maximum temperatures (Tmax) from weather stations close to the herds. Two types of ratios (i.e., ratio for WSI and odds ratio for FR) were used to identify the critical weeks for heat stress by comparing the respective measures for two sow groups based on Tmax in different weeks around weaning or service events. The ratios for WSI were calculated between groups of sows exposed to Tmax ≥ 27 °C or <27 °C in each week before weaning, with the Tmax cutoff value based on a recent review study. Similarly, the odds ratios for FR for the two groups were calculated in weeks around service. The weeks with the largest differences in the fertility measures between the two Tmax groups (i.e., the highest ratio for WSI and the lowest odds ratio for FR) were considered to be the critical weeks for heat stress. Also, piecewise models with different breakpoints were constructed to identify the threshold Tmax in the critical week. The breakpoint in the best-fit model was considered to be the threshold Tmax. The highest ratios for WSI were obtained at 1 to 3 wk before weaning in parity 1 and 2 or higher sow groups. The threshold Tmax leading to prolonged WSI was 17 °C for parity 1 sows and 25 °C for parity 2 or higher sows. Increasing Tmax by 10 °C above these thresholds increased WSI by 0.65, and 0.33 to 0.35 d, respectively (P < 0.01). For FR, the lowest odds ratios were obtained at 2 to 3 wk before service in parity 0, 1, and 2 or higher sow groups. The threshold Tmax leading to reductions in FR was 20, 21, and 24 to 25 °C for parity 0, 1, and 2 or higher sow groups, respectively. Increasing Tmax by 10 °C above these thresholds decreased FR by 3.0%, 4.3%, and 1.9% to 2.8%, respectively (P < 0.01). These results indicate that the critical weeks for heat stress were 2 to 3 wk before service for FR and 1 to 3 wk before weaning for WSI. The decreases in fertility performance in parity 0 to 1 sows started at temperatures 3 to 8 °C lower than in parity 2 or higher sows.

Patterns of Birth and Family Planning Acceptor Rates in Ghana: An Ecological Study

Despite a reported decline in Ghana's birth rate (BR), the pattern of ecological percent decrease in BR as corresponding to the percent increase in family planning acceptor rate (FPAR) in Ghana is not clear. This study explicitly explored and compared the pattern of birth and FPAR in Ghana from 2004-2015. National FPAR and BR data were retrieved from Ghana Health Service and World Bank. A time- trend descriptive analysis was performed via tableau software. Additionally, a segmented regression was applied to inferentially identify where statistically significant log-linear distinct segments exist in the trends. All segmented-related analysis was performed using joinpoint trend analysis software. Whereas, the highest decline in BR was observed from 2013-2015 (-1.4%), the highest increase in FPAR was rather observed from 2004-2008 (7.4%). Unexpectedly, from 2008-2013, a much higher decrease in FPAR (-5.8%) also yielded a moderate decline in BR (-0.7%). FPAR over the eleven years (2004-2015) increased by 1.1% whereas BR declined by -0.7%. BR in Ghana continues to be on a moderate declining trend. However, the decline was uninterrupted by an increase or decrease in FPAR. For a further decrease in Ghana's birth rate, a multifaceted approach is needed, not only focusing on increasing FPAR but also targeting adherence to FP control methods.

Fast Grid Search and Bootstrap-based Inference for Continuous Two-phase Polynomial Regression Models

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.

Three Statistical Approaches for Assessment of Intervention Effects: A Primer for Practitioners

INTRODUCTION

Statistical methods to assess the impact of an intervention are increasingly used in clinical research settings. However, a comprehensive review of the methods geared toward practitioners is not yet available.

METHODS AND MATERIALS

We provide a comprehensive review of three methods to assess the impact of an intervention: difference-in-differences (DID), segmented regression of interrupted time series (ITS), and interventional autoregressive integrated moving average (ARIMA). We also compare the methods, and provide illustration of their use through three important healthcare-related applications.

RESULTS

In the first example, the DID estimate of the difference in health insurance coverage rates between expanded states and unexpanded states in the post-Medicaid expansion period compared to the pre-expansion period was 5.93 (95% CI, 3.99 to 7.89) percentage points. In the second example, a comparative segmented regression of ITS analysis showed that the mean imaging order appropriateness score in the emergency department at a tertiary care hospital exceeded that of the inpatient setting with a level change difference of 0.63 (95% CI, 0.53 to 0.73) and a trend change difference of 0.02 (95% CI, 0.01 to 0.03) after the introduction of a clinical decision support tool. In the third example, the results from an interventional ARIMA analysis show that numbers of creatinine clearance tests decreased significantly within months of the start of eGFR reporting, with a magnitude of drop equal to -0.93 (95% CI, -1.22 to -0.64) tests per 100,000 adults and a rate of drop equal to 0.97 (95% CI, 0.95 to 0.99) tests per 100,000 per adults per month.

DISCUSSION

When choosing the appropriate method to model the intervention effect, it is necessary to consider the structure of the data, the study design, availability of an appropriate comparison group, sample size requirements, whether other interventions occur during the study window, and patterns in the data.

Breast cancer among Asian Indian and Pakistani Americans: A surveillance, epidemiology and end results-based study

Breast cancer incidence is increasing among Asian Indian and Pakistani women living in the United States. We examined the characteristics of breast cancer in Asian Indian and Pakistani American (AIPA) and non-Hispanic white (NHW) women using data from the surveillance, epidemiology and end results (SEER) program. Breast cancer incidence rates were estimated via segmented Poisson regression using data between 1990 and 2014 from SEER 9 registries, including New Jersey and California. Disease characteristics, treatment and survival information between 2000 and 2016 for 4900 AIPA and 482 250 NHW cases diagnosed after age 18 were obtained from SEER 18 registries and compared using descriptive analyses and multivariable competing risk proportional hazards regression. Breast cancer incidence was lower in AIPA than NHW women, increased with age and the rate of increase declined after age of 46 years. AIPA women were diagnosed at significantly younger age (mean (SD) = 54.5 (13.3) years) than NHW women (mean (SD) = 62 (14) years, P < .0001) and were more likely than NHW cases (P < .0001) to have regional or distant stage, higher grade, estrogen receptor-negative, progesterone receptor-negative, triple-negative or human epidermal growth factor receptor 2-enriched tumors, subcutaneous or total mastectomy, and lower cumulative incidence of death due to breast cancer (hazard ratio = 0.79, 95% CI: 0.72-0.86, P < .0001). AIPA had shorter median follow-up (52 months) than NHW cases (77 months). Breast cancer in AIPA women has unique characteristics that need to be further studied along with a comprehensive evaluation of their follow-up patterns.

Breast Cancer Screening Trends among Lower Income Women of New York: A Time-Series Evaluation of a Population-Based Intervention

Objective

This study aimed to compare the screening rate trends of mammography among New York State's lower-income women and the higher-income women from 1988 to 2010, and evaluate the potential influence of New York State's Breast Cancer Early Detection Program (introduced in 1994) on the mammography use rates of lower-income women.

Materials and Methods

Lower-income women are defined as women aged 40 and over whose household income is lower than 250% of the single member household federal poverty level (FPL) in the year that they participated in the survey. Higher-income women are defined as women aged 40 and over whose income is greater than 250% of the five-person household FPL. Data were obtained from the Behavioral Risk Factor Surveillance System. Interrupted time series analysis was conducted to examine screening rates before and after the launch of the Breast Cancer Early Detection program.

Results

Among the lower-income women, the pre-intervention mammography screening rate significantly increased by an average of 15.21% every two years. However, after implementation of the Breast Cancer Early Detection Program, this rate of increase significantly slowed (slope change=-13.67, p=0.00016). The lower-income women and the higher-income women experienced a similar trend change after the intervention started.

Conclusion

This study found limited evidence that the Breast Cancer Early Detection Programme significantly contributed to the state-wide increase in mammography screening rate among lower-income women from 1988 to 2010. Future studies should examine the influence of structural and individual barriers inhibiting uptake of mammography screening among lower-income women.

Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data

BACKGROUND

In the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such 'aggregate-level' analysis is biased when data are missing at random (MAR) and provide alternative analysis methods.

METHODS

Using electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment.

RESULTS

Aggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed models with multiple imputation can be used with other types of ITS outcomes (eg, proportions). Other standard methods applied in ITS do not help to correct this bias problem.

CONCLUSION

Aggregate-level SR analysis can bias the ITS estimates when individual-level data are MAR, because taking averages of individual-level data before SR means that data at the cluster level are missing not at random. Avoiding the averaging-step and using mixed models with or without multilevel multiple imputation of covariates is recommended.

Design, analysis, power, and sample size calculation for three-phase interrupted time series analysis in evaluation of health policy interventions

OBJECTIVE

To discuss the study design and data analysis for three-phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies.

METHODS

We consider the design and analysis of three-phase ITS studies using a study funded by National Institutes of Health as an exemplar. The design and analysis of both one-arm and two-arm three-phase ITS studies are introduced.

RESULTS

A simulation-based approach, with ready-to-use computer programs, was developed to determine the power for two types of three-phase ITS studies. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 with various effect sizes. The power increased as the sample size or the effect size increased. The power to detect the same effect sizes varied largely, depending on testing level change, trend changes, or both.

CONCLUSION

This article provides a convenient tool for investigators to generate sample sizes to ensure sufficient statistical power when three-phase ITS study design is implemented.

Impact of a Multifaceted Pharmacist-Led Intervention on Antimicrobial Stewardship in a Gastroenterology Ward: A Segmented Regression Analysis

BACKGROUND

Irrational use of antimicrobial agents for gastrointestinal diseases deserves attention, but corresponding antimicrobial stewardship programs (ASPs) are generally not a priority for managers. We conducted this study to evaluate the effectiveness of multifaceted pharmacist-led (MPL) interventions in the gastroenterology ward (GW) to provide evidence for the efficacy of ASPs in a non-priority department.

METHODS

This was an interventional, retrospective study implemented in China. The MPL intervention lasting 1.5 years involved daily ward rounds with physicians, regular review of medical orders, monthly indicator feedback, frequent physician training, and necessary patient education. Data on all hospitalized adults receiving antibiotics was extracted from the hospital information system over a 36-month period from January 2016 to December 2018. Segmented regression analysis of interrupted time series was performed to evaluate the effect of the MPL interventions (started in July 2017) on antibiotic use and length of hospital stay, which was calculated monthly as analytical units.

RESULTS

A total of 1763 patients receiving antibiotics were enrolled. Segmented regression models showed descending trends from the baseline in the intensity of antibiotic consumption (coefficient = -0.88, p = 0.01), including a significant decline in the level of change of the proportion of patients receiving combined antibiotics (coefficient = -9.91, p = 0.03) and average length of hospital stay (coefficient = -1.79, p = 0.00), after MPL interventions. The MPL interventions led to a temporary increase in the proportion of patients receiving antibiotics (coefficient = 4.95, p = 0.038), but this was part of a declining secular trend (coefficient = -0.45, p = 0.05).

CONCLUSION

The MPL interventions led a statistically significant decline in the number of patients receiving antibiotics, the antibiotic consumption, and the average hospital stay post-intervention compared to the pre-intervention phase of the study. Health policymakers should actively practice MPL interventions by clinical pharmacists in ASPs in those departments that are not included in priority management.

Trends in seasonal influenza vaccine coverage of target groups in France, 2006/07 to 2015/16: Impact of recommendations and 2009 influenza A(H1N1) pandemic

Background and aimsSeasonal influenza vaccination (SIV) uptake (SIVU) rates in France are below target. We (i) describe trends in French SIVU over 10 consecutive seasons among different target groups and (ii) examine the effects of the 2009 influenza A(H1N1) pandemic and the publication of new SIV recommendations in 2011 and 2013.MethodsOur study was based on records of vaccines delivered in community pharmacies for a permanent, representative sample of 805,000 beneficiaries of the French National Health Insurance Fund. For the first objective, we analysed SIVU rate trends among ≥ 65 year olds as well as among  < 65 year olds with each of the following conditions: diabetes, respiratory, cardiovascular, neuromuscular, or chronic liver disease. For the second goal, we computed segmented log-binomial regression analyses.ResultsAfter the 2009 pandemic, except for the target group with liver diseases, where the difference was not statistically significant, SIVU fell significantly in all groups during the 2010/11 season, remaining relatively stable until 2015/16 in groups not targeted by new recommendations. Crude SIVU rates in 2015/16 were 48% (43,950/91,794) for ≥ 65 year olds and between 16% (407/2,565) and 29% (873/3,056) for  < 65 year olds depending on their condition. SIVU increased modestly after new recommendations were published, but only in patients newly eligible for a free vaccine voucher.ConclusionsOur results suggest: (i) a prolonged confidence crisis in SIV, initially impelled by the 2009 pandemic vaccination campaign; (ii) that new recommendations are ineffective without additional measures. Interventional research in this field is a priority.

Fast Bootstrap Confidence Intervals for Continuous Threshold Linear Regression

Continuous threshold regression is a common type of nonlinear regression that is attractive to many practitioners for its easy interpretability. More widespread adoption of thresh-old regression faces two challenges: (i) the computational complexity of fitting threshold regression models and (ii) obtaining correct coverage of confidence intervals under model misspecification. Both challenges result from the non-smooth and non-convex nature of the threshold regression model likelihood function. In this paper we first show that these two issues together make the ideal approach for making model-robust inference in continuous threshold linear regression an impractical one. The need for a faster way of fitting continuous threshold linear models motivated us to develop a fast grid search method. The new method, based on the simple yet powerful dynamic programming principle, improves the performance by several orders of magnitude.

Segmented Bayesian calibration approach for estimating age in forensic science

Forensic age estimation is receiving growing attention from researchers in the last few years. Accurate estimates of age are needed both for identifying real age in individuals without any identity document and assessing it for human remains. The methods applied in such context are mostly based on radiological analysis of some anatomical districts and entail the use of a regression model. However, estimating chronological age by regression models leads to overestimated ages in younger subjects and underestimated ages in older ones. We introduced a full Bayesian calibration method combined with a segmented function for age estimation that relied on a Normal distribution as a density model to mitigate this bias. In this way, we were also able to model the decreasing growth rate in juveniles. We compared our new Bayesian-segmented model with other existing approaches. The proposed method helped producing more robust and precise forecasts of age than compared models while exhibited comparable accuracy in terms of forecasting measures. Our method seemed to overcome the estimation bias also when applied to a real data set of South-African juvenile subjects.

Vitamin D and Anti-Müllerian Hormone Levels in Infertility Treatment: The Change-Point Problem

Background: Anti-Müllerian hormone (AMH) is considered to be one of the most significant indicators of women’s fertility. Many studies have shown that vitamin D may modify human reproductive functions; however, the results are conflicting. The composition of follicular fluid (FF) creates the biochemical environment of the oocyte and affects its quality, which later determines the embryo quality. In this study, we aimed to revise with advanced statistical techniques the relationship between AMH and vitamin D in FF. Methods: The study was designed as a prospective single-center study in infertile patients with AMH ≥ 0.7 ng/mL who underwent controlled ovarian hyperstimulation for in vitro fertilization. AMH and vitamin D levels in FF were measured. Next, the standard and advanced statistical (including segmented regression) techniques were applied. Results: We observed a negative linear correlation between levels of AMH in serum and FF and total vitamin D concentrations up to approximately 30 ng/mL; with a statistically significant relationship in FF. Beyond that concentration, the trend was positive but statistically insignificant. Conclusions: As an existing “change-point problem” was noticed, we suggest segmentation in the relationship between vitamin D and AMH during infertility treatment.

Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: protocol for a review

INTRODUCTION

An interrupted time series (ITS) design is an important observational design used to examine the effects of an intervention or exposure. This design has particular utility in public health where it may be impracticable or infeasible to use a randomised trial to evaluate health system-wide policies, or examine the impact of exposures (such as earthquakes). There have been relatively few studies examining the design characteristics and statistical methods used to analyse ITS designs. Further, there is a lack of guidance to inform the design and analysis of ITS studies.This is the first study in a larger project that aims to provide tools and guidance for researchers in the design and analysis of ITS studies. The objectives of this study are to (1) examine and report the design characteristics and statistical methods used in a random sample of contemporary ITS studies examining public health interventions or exposures that impact on health-related outcomes, and (2) create a repository of time series data extracted from ITS studies. Results from this study will inform the remainder of the project which will investigate the performance of a range of commonly used statistical methods, and create a repository of input parameters required for sample size calculation.

METHODS AND ANALYSIS

We will collate 200 ITS studies evaluating public health interventions or the impact of exposures. ITS studies will be identified from a search of the bibliometric database PubMed between the years 2013 and 2017, combined with stratified random sampling. From eligible studies, we will extract study characteristics, details of the statistical models and estimation methods, effect metrics and parameter estimates. Further, we will extract the time series data when available. We will use systematic review methods in the screening, application of inclusion and exclusion criteria, and extraction of data. Descriptive statistics will be used to summarise the data.

ETHICS AND DISSEMINATION

Ethics approval is not required since information will only be extracted from published studies. Dissemination of the results will be through peer-reviewed publications and presentations at conferences. A repository of data extracted from the published ITS studies will be made publicly available.

Assessing health care interventions via an interrupted time series model: Study power and design considerations

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.

Cardiovascular Disease Hospitalizations in Louisiana Parishes' Elderly before, during and after Hurricane Katrina

The research on how health and health care disparities impact response to and recovery from a disaster, especially among diverse and underserved populations is in great need for a thorough evaluation. The time series analysis utilizing most complete national databases of medical records is an indispensable tool in assessing the destruction and health toll brought about by natural disasters. In this study, we demonstrated such an application by evaluating the impact of Hurricane Katrina in 2005 on cardiovascular disease (CVD), a primary cause of mortality among older adults that can be aggravated by natural disasters. We compared CVD hospitalizations before, during and after Katrina between white and black residents of three most populated parishes in Louisiana: Orleans and Jefferson, which were severely affected by the landfall and subsequent floods, and East Baton Rouge, which hosted many of the evacuees. We abstracted 383,552 CVD hospitalization records for Louisiana's patients aged 65+ in 2005⁻2006 from the database maintained by the Center of Medicare &amp; Medicaid Services. Daily time series of CVD-related hospitalization rates at each study parish were compiled, and the changes were characterized using segmented regression. In Orleans Parish, directly affected by the hurricane, hospitalization rates peaked on the 6th day after landfall with an increase (mean ± SD) from 7.25 ± 2.4 to 18.5 ± 17.3 cases/day per 10,000 adults aged 65+ (p < 0.001) and returned to pre-landfall level after ~2 months. Disparities in CVD rates between black and white older adults were exacerbated during and following landfall. In Orleans Parish, a week after landfall, the CVD rates increased to 26.3 ± 23.7 and 16.6 ± 11.7 cases/day per 10,000 people (p < 0.001) for black and white patients, respectively. The abrupt increase in CVDs is likely due to psychosocial and post-traumatic stress caused by the disaster and inadequate response. Inequities in resource allocation and access have to be addressed in disaster preparation and mitigation.

Prescribing Variation in General Practices in England Following a Direct Healthcare Professional Communication on Mirabegron

Introduction: Pharmacovigilance may detect safety issues after marketing of medications, and this can result in regulatory action such as direct healthcare professional communications (DHPC). DHPC can be effective in changing prescribing behaviour, however the extent to which prescribers vary in their response to DHPC is unknown. This study aims to explore changes in prescribing and prescribing variation among general practitioner (GP) practices following a DHPC on the safety of mirabegron, a medication to treat overactive bladder (OAB). Methods: This is an interrupted time series study of English GP practices from 2014–2017. National Health Service (NHS) Digital provided monthly statistics on aggregate practice-level prescribing and practice characteristics (practice staff and registered patient profiles, Quality and Outcomes Framework indicators, and deprivation of the practice area). The primary outcome was monthly mirabegron prescriptions as a percentage of all OAB drug prescriptions and we assessed the change following a DHPC issued by the European Medicines Agency in September 2015. The DHPC stated mirabegron use was contraindicated with severe uncontrolled hypertension and cautioned with hypertension. Variation between practices in mirabegron prescribing before and after the DHPC was assessed using the systematic component of variation (SCV). Multilevel segmented regression with random effects quantified the change in level and trend of prescribing after the DHPC. Practice characteristics were assessed for their association with a reduction in prescribing following the DHPC. Results: This study included 7408 practices. During September 2015, 88.9% of practices prescribed mirabegron and mirabegron comprised a mean of 8.2% (SD 6.8) of OAB prescriptions. Variation between practices was classified as very high and the median SCV did not change significantly (p = 0.11) in the six months after the September 2015 DHPC (12.4) compared to before (11.6). Before the DHPC, the share of mirabegron over all OAB drug prescriptions increased by 0.294 (95% confidence interval (CI), 0.287, 0.301) percentage points per month. There was no significant change in the month immediately after the DHPC (−0.023, 95% CI −0.105 to 0.058), however there was a significant reduction in trend (−0.036, 95% CI −0.049 to −0.023). Higher numbers of registered patients, patients aged ≥65 years, and practice area deprivation were associated with having a significant decrease in level and slope of mirabegron prescribing post-DHPC. Conclusion: Variation in mirabegron prescribing was high over the study period and did not change substantively following the DHPC. There was no immediate prescribing change post-DHPC, although the monthly growth did slow. Knowledge of the degree of variation in and determinants of response to safety communications may allow those that do not change prescribing habits to be provided with additional support.

Methods, applications, interpretations and challenges of interrupted time series (ITS) data: protocol for a scoping review

OBJECTIVES

Interrupted time series (ITS) design involves collecting data across multiple time points before and after the implementation of an intervention to assess the effect of the intervention on an outcome. ITS designs have become increasingly common in recent times with frequent use in assessing impact of evidence implementation interventions. Several statistical methods are currently available for analysing data from ITS designs; however, there is a lack of guidance on which methods are optimal for different data types and on their implications in interpreting results. Our objective is to conduct a scoping review of existing methods for analysing ITS data, to summarise their characteristics and properties, as well as to examine how the results are reported. We also aim to identify gaps and methodological deficiencies.

METHODS AND ANALYSIS

We will search electronic databases from inception until August 2016 (eg, MEDLINE and JSTOR). Two reviewers will independently screen titles, abstracts and full-text articles and complete the data abstraction. The anticipated outcome will be a summarised description of all the methods that have been used in analysing ITS data in health research, how those methods were applied, their strengths and limitations and the transparency of interpretation/reporting of the results. We will provide summary tables of the characteristics of the included studies. We will also describe the similarities and differences of the various methods.

ETHICS AND DISSEMINATION

Ethical approval is not required for this study since we are just considering the methods used in the analysis and there will not be identifiable patient data. Results will be disseminated through open access peer-reviewed publications.

Segmented linear modeling of CHO fed-batch culture and its application to large scale production

We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed‐batch cultures. Using the model structure and parameter values from a small‐scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed‐batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785–797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Historical Data Analysis of Hospital Discharges Related to the Amerithrax Attack in Florida

Interrupted time-series analysis (ITSA) can be used to identify, quantify, and evaluate the magnitude and direction of an event on the basis of time-series data. This study evaluates the impact of the bioterrorist anthrax attacks ("Amerithrax") on hospital inpatient discharges in the metropolitan statistical area of Palm Beach, Broward, and Miami-Dade counties in the fourth quarter of 2001. Three statistical methods-standardized incidence ratio (SIR), segmented regression, and an autoregressive integrated moving average (ARIMA)-were used to determine whether Amerithrax influenced inpatient utilization. The SIR found a non-statistically significant 2 percent decrease in hospital discharges. Although the segmented regression test found a slight increase in the discharge rate during the fourth quarter, it was also not statistically significant; therefore, it could not be attributed to Amerithrax. Segmented regression diagnostics preparing for ARIMA indicated that the quarterly data time frame was not serially correlated and violated one of the assumptions for the use of the ARIMA method and therefore could not properly evaluate the impact on the time-series data. Lack of data granularity of the time frames hindered the successful evaluation of the impact by the three analytic methods. This study demonstrates that the granularity of the data points is as important as the number of data points in a time series. ITSA is important for the ability to evaluate the impact that any hazard may have on inpatient utilization. Knowledge of hospital utilization patterns during disasters offer healthcare and civic professionals valuable information to plan, respond, mitigate, and evaluate any outcomes stemming from biothreats.

Trend of Smear-positive Pulmonary Tuberculosis in Iran during 1995-2012: A Segmented Regression Model

Background:

Describing trend in tuberculosis (TB) over time can play an important role to assess the disease control strategies and predict the future morbidity and mortality. This study aimed to determine the incidence trend of smear-positive pulmonary tuberculosis (SPPT) in sub-age and sex groups during the years of 1995–2012.

Methods:

This retrospective cohort study was performed in 2015 by using the dataset regarding National Statistics of SPPT reported by World Health Organization during 1995–2012. Annual percent changes (APCs) and average annual percent changes (AAPCs) were estimated to determine the summery statistics of trend using segmented regression model.

Results:

During 1995–2012, there were 96,579 SPPT case notifications in Iran (male to female ratio: 0.99). There was only one change point in 1997 for SPPT incidence in subgroups of age and sex during 1995–2012. The AAPCs for both genders and also all three age groups had a significant descending trend during the time period (P < 0.05).

Conclusions:

Our results showed a downward trend in the SPPT incidence. It seems that to achieve the set goals and high successful in TB control program especially reduction in SPPT, pay more attention to old age and males should be considered. In addition, improvement of clinical and medical care services and notification processes would be imperative.

Estimating the Smoking Ban Effects on Smoking Prevalence, Quitting and Cigarette Consumption in a Population Study of Apprentices in Italy

Objectives: We evaluated the effects of the Italian 2005 smoking ban in public places on the prevalence of smoking, quitting and cigarette consumption of young workers. Data and Methods: The dataset was obtained from non-computerized registers of medical examinations for a population of workers with apprenticeship contracts residing in the province of Viterbo, Italy, in the period 1996–2007. To estimate the effects of the ban, a segmented regression approach was used, exploiting the discontinuity introduced by the application of the law on apprentices’ smoking behavior. Results: It is estimated that the Italian smoking ban generally had no effect on smoking prevalence, quitting ratio, or cigarette consumption of apprentices. However, when the estimates were applied to subpopulations, significant effects were found: −1% in smoking prevalence, +2% in quitting, and −3% in smoking intensity of apprentices with at least a diploma.

Choice of statistical method influences apparent association between structure and function in glaucoma

PURPOSE

The aim of this study was to explore how different statistical methods may lead to inconsistent inferences about the association between structure and function in glaucoma.

METHODS

Two datasets from published studies were selected for their illustrative value. The first consisted of measurements of neuroretinal rim area in the superior-temporal sector paired with the corresponding visual field sensitivity. The second consisted of measurements of average retinal nerve fiber layer thickness over all sectors paired with the corresponding visual field sensitivity. Statistical methods included linear and segmented regression, and a nonparametric local-linear fit known as loess. The analyses were repeated with all measurements expressed as percent of mean normal.

RESULTS

Slopes from linear fits to the data changed by a factor of 10 depending on the linear regression method applied. Inferences about whether structural abnormality precedes functional abnormality varied with the statistical design and the units of measure used.

CONCLUSIONS

The apparent association between structure and function in glaucoma, and consequent interpretation, varies with the statistical method and units of measure. Awareness of the limitations of any statistical analysis is necessary to avoid finding spurious results that ultimately may lead to inadequate clinical recommendations.