Timing the clinical onset of epileptic spasms in infantile epileptic spasms syndrome: A tertiary health center's experience.

OBJECTIVE: Lead time to treatment (clinical onset of epileptic spasms [ES] to initiation of appropriate treatment) is known to predict outcomes in infantile epileptic spasms syndrome (IESS). Timing the clinical onset of ES is crucial to establish lead time. We investigated how often ES onset could be established to the nearest week. We aimed to (1) ascertain the exact date or estimate the nearest week of ES onset and (2) compare clinical/demographic factors between patients where date of ES onset was determined or estimated to the nearest week and patients whose date of ES onset could not be estimated to the nearest week. Reasons for difficulties in estimating date of ES onset were explored. METHODS: Retrospective chart review of new onset IESS patients (January 2019-May 2022) extracted the date or week of the clinical onset of ES. Predictors of difficulty in date of ES onset estimation to the nearest week were examined by regression analysis. Sources contributing to difficulties determining date of ES onset were assessed after grouping into categories (provider-, caregiver-, disease-related). RESULTS: Among 100 patients, date of ES onset was estimated to the nearest week in 47%. On univariable analysis, age at diagnosis (p = .021), development delay (p = .007), developmental regression/stagnation (p = .021), ES intermixed with other seizures (p = .011), and nonclustered ES at onset (p = .005) were associated with difficulties estimating date of ES onset. On multivariable analysis, failure to establish date of ES onset was related to ES intermixed with other seizures (p = .004) and nonclustered ES at onset (p = .003). Sources contributing to difficulties determining date of ES onset included disease-related factors (ES characteristics, challenges interpreting electroencephalograms) and provider/caregiver-related factors (delayed diagnosis). SIGNIFICANCE: Difficulties with estimation of lead time (due to difficulties timing ES onset) can impact clinical care (prognostication), as even small increments in lead time duration can have adverse developmental consequences.

Surveillance after surgery for pancreatic cancer: a global scoping review of guidelines and a nordic Survey of contemporary practice.

OBJECTIVES: Most patients with pancreatic cancer who have undergone surgical resection eventually develop disease recurrence. |This study aimed to investigate whether there is evidence to support routine surveillance after pancreatic cancer surgery, with a secondary aim of analyzing the implementation of surveillance strategies in the Nordic countries. MATERIALS AND METHODS: A scoping review was conducted to identify clinical practice guidelines globally and research studies relating to surveillance after pancreatic cancer resection. This was followed by a survey among 20 pancreatic units from four Nordic countries to assess their current practice of follow-up for operated patients. RESULTS: Altogether 16 clinical practice guidelines and 17 research studies were included. The guidelines provided inconsistent recommendations regarding postoperative surveillance of pancreatic cancer. The clinical research data were mainly based on retrospective cohort studies with low level of evidence and lead-time bias was not addressed. Active surveillance was recommended in Sweden and Denmark, but not in Norway beyond the post-operative/adjuvant period. Finland had no national recommendations for surveillance. The Nordic survey revealed a wide variation in reported practice among the different units. About 75% (15 of 20 units) performed routine postoperative surveillance. Routine CA 19-9 testing was used by 80% and routine CT by 67% as part of surveillance. About 73% of centers continued follow-up until 5 years postoperatively. CONCLUSION: Evidence for routine long-term (i.e. 5 years) surveillance after pancreatic cancer surgery remains limited. Most pancreatic units in the Nordic countries conduct regular follow-up, but protocols vary.

First experiences of a hospital-based 3D printing facility - an analytical observational study.

PURPOSE: To identify the clinical impact and potential benefits of in-house 3D-printed objects through a questionnaire, focusing on three principal areas: patient education; interdisciplinary cooperation; preoperative planning and perioperative execution. MATERIALS AND METHODS: Questionnaires were sent from January 2021 to August 2022. Participants were directed to rate on a scale from 1 to 10. RESULTS: The response rate was 43%. The results of the rated questions are averages. 84% reported using 3D-printed objects in informing the patient about their condition/procedure. Clinician-reported improvement in patient understanding of their procedure/disease was 8.1. The importance of in-house placement was rated 9.2. 96% reported using the 3D model to confer with colleagues. Delay in treatment due to 3D printing lead-time was 1.8. The degree with which preoperative planning was altered was 6.9. The improvement in clinician perceived preoperative confidence was 8.3. The degree with which the scope of the procedure was affected, in regard to invasiveness, was 5.6, wherein a score of 5 is taken to mean unchanged. Reduction in surgical duration was rated 5.7. CONCLUSION: Clinicians report the utilization of 3D printing in surgical specialties improves procedures pre- and intraoperatively, has a potential for increasing patient engagement and insight, and in-house location of a 3D printing center results in improved interdisciplinary cooperation and allows broader access with only minimal delay in treatment due to lead-time.

Uttarakhand State Earthquake Early Warning System: A Case Study of the Himalayan Environment.

The increased seismic activity observed in the Himalayas, coupled with the expanding urbanization of the surrounding areas in northern India, poses significant risks to both human lives and property. Developing an earthquake early warning system in the region could help in alleviating these risks, especially benefiting cities and towns in mountainous and foothill regions close to potential earthquake epicenters. To address this concern, the government and the science and engineering community collaborated to establish the Uttarakhand State Earthquake Early Warning System (UEEWS). The government of Uttarakhand successfully launched this full-fledged operational system to the public on 4 August 2021. The UEEWS includes an array of 170 accelerometers installed in the seismogenic areas of the Uttarakhand. Ground motion data from these sensors are transmitted to the central server through the dedicated private telecommunication network 24 hours a day, seven days a week. This system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile app freely available for smartphone users and by blowing sirens units installed in the buildings earmarked by the government. The UEEWS has successfully issued alerts for light earthquakes that have occurred in the instrumented region and warnings for moderate earthquakes that have triggered in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of instrumentation, adaptation of attributes and their relation to earthquake parameters, operational flow of the system, and information about dissemination of warnings to the public.

Long Short-Term Memory Networks' Application on Typhoon Wave Prediction for the Western Coast of Taiwan.

Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly.

Incidental Findings and Low-Value Care.

Incidental imaging findings are common and analogous to the results of screening tests when screening is performed of unselected, low-risk patients. Approximately 15-30% of all diagnostic imaging and 20-40% of CT examinations contain at least one incidental finding. Patients with incidental findings but low risk for disease are likely to experience length bias, lead-time bias, overdiagnosis, and overtreatment that create an illusion of benefit while conferring harm. This includes incidental detection of many types of cancers that, although malignant, would have been unlikely to affect a patient's health had the cancer not been detected. Detection of some incidental findings can improve health, but most do not. Greater patient- and disease-related risk increase the likelihood an incidental finding is important. Clinical guidelines for incidental findings should more deeply integrate patient risk factors and disease aggressiveness to inform management. Lack of outcome and cost-effectiveness data has led to reflexive management strategies for incidental findings that promote low-value and sometimes harmful care.

Factors associated with appointment 'no-shows' at two tertiary level outpatient oculoplastic clinics.

PURPOSE: Appointment no-shows in clinics can adversely impact patients and physicians alike. This study aimed to determine the rate and potential causes of missed appointments in oculoplastic clinics and compare a private practice and hospital-based academic setting. METHODS: A retrospective review of patients who booked appointments for oculoplastic consultation, between August 2019 and January 2020 at two oculoplastic clinics was performed. Demographic and patient-specific characteristics of patients who failed to attend their appointment were identified. Data were analysed to determine and compare the no-show rates in both clinics and logistic regression was performed to determine factors associated with them. RESULTS: The rate of missed appointments was 3% and 17% at the oculoplastic clinics of Lions Eye Institute (LEI, private practice) and Albany Medical Center (AMC, academic hospital-based office), respectively. Patients at the AMC clinic were more likely to be male, younger, have a lower household income, not carry private insurance, and suffer from trauma. Logistic regression analysis showed lower patient age to significantly increase the likelihood of no-shows in both clinics (p = .01 for LEI, p = .003 for AMC), and lead appointment time greater than 90 days to be a significant risk factor for no-shows at LEI (p = .01). CONCLUSIONS: The no-show rate for oculoplastic appointments is 3% and 17% at LEI and AMC clinics, respectively. Our analysis shows that younger patients are more likely to miss appointments at both clinics, and an appointment lead time greater than 90 days is a significant risk factor for no-shows at LEI.

Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain.

Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the population. India may find it difficult to achieve this service level target, owing to several infrastructural deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed as critical nodes and must be overhauled structurally to make the implementation of strategic inventory policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to identify critical nodes and structurally overhaul them later for efficiently achieving service level targets.

Assessing lead time bias due to mammography screening on estimates of loss in life expectancy.

BACKGROUND: An increasingly popular measure for summarising cancer prognosis is the loss in life expectancy (LLE), i.e. the reduction in life expectancy following a cancer diagnosis. The proportion of life lost (PLL) can also be derived, improving comparability across age groups as LLE is highly age-dependent. LLE and PLL are often used to assess the impact of cancer over the remaining lifespan and across groups (e.g. socioeconomic groups). However, in the presence of screening, it is unclear whether part of the differences across population groups could be attributed to lead time bias. Lead time is the extra time added due to early diagnosis, that is, the time from tumour detection through screening to the time that cancer would have been diagnosed symptomatically. It leads to artificially inflated survival estimates even when there are no real survival improvements. METHODS: In this paper, we used a simulation-based approach to assess the impact of lead time due to mammography screening on the estimation of LLE and PLL in breast cancer patients. A natural history model developed in a Swedish setting was used to simulate the growth of breast cancer tumours and age at symptomatic detection. Then, a screening programme similar to current guidelines in Sweden was imposed, with individuals aged 40-74 invited to participate every second year; different scenarios were considered for screening sensitivity and attendance. To isolate the lead time bias of screening, we assumed that screening does not affect the actual time of death. Finally, estimates of LLE and PLL were obtained in the absence and presence of screening, and their difference was used to derive the lead time bias. RESULTS: The largest absolute bias for LLE was 0.61 years for a high screening sensitivity scenario and assuming perfect screening attendance. The absolute bias was reduced to 0.46 years when the perfect attendance assumption was relaxed to allow for imperfect attendance across screening visits. Bias was also present for the PLL estimates. CONCLUSIONS: The results of the analysis suggested that lead time bias influences LLE and PLL metrics, thus requiring special consideration when interpreting comparisons across calendar time or population groups.

Development and evaluation of safety and effectiveness of novel cancer screening tests for routine clinical use with applications to multicancer detection technologies.

Multicancer screening is a promising approach to improving the detection of preclinical disease, but current technologies have limited ability to identify precursor or early stage lesions, and approaches for developing the evidentiary chain are unclear. Frameworks to enable development and evaluation from discovery through evidence of clinical effectiveness are discussed.

Multi-step ahead prediction of hourly influent characteristics for wastewater treatment plants: a case study from North America.

Prediction of influent characteristics, before any treatment takes place, is of great importance to the operation and management of wastewater treatment plants (WWTPs). In this study, four machine-learning models, including multilayer perceptron (MLP), long short-term memory network (LSTM), K-nearest neighbour (KNN), and random forest (RF), are introduced to utilize real-time wastewater data from three WWTPs in North America (i.e., Tres Rios, Woodward, and one confidential plant) for predicting hourly influent characteristics. Input variables are selected using an autocorrelation analysis and a variable importance measure from RF. Both univariate and multivariate analyses are investigated to improve model accuracy. The performances of one- and multiple-step-ahead models are compared. With a short prediction horizon, all the models derived from both univariate and multivariate analyses show excellent performance. It was found that the performance deterioration as the prediction horizon expands could be mitigated significantly by including extra variables, such as meteorological variables. This work can provide valuable support for the high-temporal-resolution prediction of wastewater influent characteristics for WWTPs. The proposed models can also bridge the gap between data and decision-making in the wastewater sector.

Identification of the cause of fall during the pre-impact fall period.

[Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant's waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0-2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method.

A multistate survival model of the natural history of cancer using data from screened and unscreened population.

One of the main aims of models using cancer screening data is to determine the time between the onset of preclinical screen-detectable cancer and the onset of the clinical state of the cancer. This time is called the sojourn time. One problem in using screening data is that an individual can be observed in preclinical phase or clinically diagnosed but not both. Multistate survival models provide a method of modeling the natural history of cancer. The natural history model allows for the calculation of the sojourn time. We developed a continuous-time Markov model and the corresponding likelihood function. The model allows for the use of interval-censored, left-truncated and right-censored data. The model uses data of clinically diagnosed cancers from both screened and nonscreened individuals. Parameters of age-varying hazards and age-varying misclassification are estimated simultaneously. The mean sojourn time is calculated from a micro-simulation using model parameters. The model is applied to data from a prostate screening trial. The simulation study showed that the model parameters could be estimated accurately.

Modeling the mean time to interval cancer after negative results of periodic cancer screening.

Interval cancers are cancers detected symptomatically between screens or after the last screen. A mathematical model for the development of interval cancers can provide useful information for evaluating cancer screening. In this regard a useful quantity is MIC, the mean duration in years of progressive preclinical cancer (PPC) that leads to interval cancers. Estimation of MIC involved extending a previous model to include three negative screens, invoking the multinomial-Poisson transformation to avoid estimating background cancer trends, and varying screening test sensitivity. Simulations show that when the true MIC is 0.5, the method yields a reasonably narrow range of estimated MICs over the range of screening test sensitivities from 0.5 to 1.0. If the lower bound on the screening test sensitivity is 0.7, the method performs considerably better even for larger MICs. The application of the method involved annual lung cancer screening in the Prostate, Lung, Colorectal, and Ovarian trial. Assuming a normal distribution for PPC duration, the estimated MIC (95% confidence interval) ranged from 0.00 (0.00 to 0.34) at a screening test sensitivity of 1.0 to 0.54 (0.03, 1.00) at a screening test sensitivity of 0.5 Assuming an exponential distribution for PPC duration, which did not fit as well, the estimated MIC ranged from 0.27 (0.08, 0.49) at a screening test sensitivity of 0.5 to 0.73 (0.32, 1.26) at a screen test sensitivity of 1.0 Based on these results, investigators may wish to investigate more frequent lung cancer screening.

Duration of lead time in screening for lung cancer.

BACKGROUND: Screening for lung cancer has used chest radiography (CR), low dose computed tomography (LDCT) and sputum cytology (SC). Estimates of the lead time (LT), i.e., the time interval from detection of lung cancer by screening to the development of symptoms, have been derived from longitudinal studies of populations at risk, tumor doubling time (DT), the ratio between its prevalence at the first round of screening and its annual incidence during follow-up, and by probability modeling derived from the results of screening trials. OBJECTIVE: To review and update the estimates of LT of lung cancer. METHODS: A non-systematic search of the literature for estimates of LT and screening trials. Search of the reference sections of the retrieved papers for additional relevant studies. Calculation of LTs derived from these studies. RESULTS: LT since detection by CR was 0.8-1.1 years if derived from longitudinal studies; 0.6-2.1 years if derived from prevalence / incidence ratios; 0.2 years if derived from the average tumor DT; and 0.2-1.0 if derived from probability modeling. LT since detection by LDCT was 1.1-3.5 if derived from prevalence / incidence ratios; 3.9 if derived from DT; and 0.9 if derived from probability modeling. LT since detection of squamous cell cancer by SC in persons with normal CR was 1.3-1.5 if derived from prevalence/incidence ratios; and 2.1 years if derived from the DT of squamous cell cancer. CONCLUSIONS: Most estimates of the LT yield values of 0.2-1.5 years for detection by CR; of 0.9-3.5 years for detection by LDCT; and about 2 years or less for detection of squamous cell cancer by SC in persons with normal CR. The heterogeneity of the screening trials and methods of derivation may account for the variability of LT estimates.

An Algorithm for Pairing Interventionalists and Surgeons for the TAVR Procedure.

The Transcatheter Aortic Valve Replacement (TAVR) procedure requires an initial consultation and a subsequent procedure by an interventionalist (IC) and surgeon. The IC-surgeon pair coordination is extremely challenging, especially at Mayo Clinic due to provider time commitments distributed across practice, research, and education activities. Current practice aims to establish the coordination manually, resulting in a scheduling process that is cumbersome and time consuming for the schedulers. We develop an algorithm for pairing ICs and surgeons that minimizes the lead time (days elapsed between the clinic consult and procedure). As compared to current practice, this algorithm is able to reduce average lead time by 59% and increase possible IC-surgeon pairs by 7%. The proposed algorithm is shown to be flexible enough to incorporate practice variations such as lead time upper bound and two procedure days for a single consult day. Algorithm alternatives are also presented for practices who may find the proposed algorithm infeasible for their practice.

Breast cancer distant recurrence lead time interval by detection method in an institutional cohort.

BACKGROUND: Lead time, the interval between screen detection and when a disease would have become clinically evident, has been cited to explain longer survival times in mammography detected breast cancer cases (BC). METHODS: An institutional retrospective cohort study of BC outcomes related to detection method (mammography (MamD) vs. patient (PtD)). Cases were first primary invasive stage I-III BC, age 40-74 years (n = 6603), 1999-2016. Survival time was divided into 1) distant disease-free interval (DDFI) and 2) distant disease-specific survival (DDSS) as two separate time interval outcomes. We measured statistical association between detection method and diagnostic, treatment and outcome variables using bivariate comparisons, Cox proportional hazards analyses and mean comparisons. Outcomes were distant recurrence (n = 422), DDFI and DDSS. RESULTS: 39% of cases were PtD (n = 2566) and 61% were MamD (n = 4037). MamD cases had a higher percentage of Stage I tumors [MamD 69% stage I vs. PtD 31%, p < .001]. Rate of distant recurrence was 11% among PtD BC cases (n = 289) vs. 3% of MamD (n = 133) (p < .001). Order of factor entry into the distant recurrence time interval (DDFI) model was 1) TNM stage (p < .001), 2) HR/HER2 status (p < .001), 3) histologic grade (p = .005) and 4) detection method (p < .001). Unadjusted PtD DDFI mean time was 4.34 years and MamD 5.52 years (p < .001), however when stratified by stage, the most significant factor relative to distant recurrence, there was no significant difference between PtD and MamD BC. Distant disease specific survival time did not differ by detection method. CONCLUSION: We observed breast cancer distant disease-free interval to be primarily associated with stage at diagnosis and tumor characteristics with less contribution of detection method to the full model. Patient and mammography detected breast cancer mean lead time to distant recurrence differed significantly by detection method for all stages but not significantly within stage with no difference in time from distant recurrence to death. Lead time difference related to detection method appears to be present but may be less influential than other factors in distant disease-free and disease specific survival.

Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: Stages of progression and potential benefits of early intervention: A retrospective study.

BACKGROUND: The frequency, nature and timeline of changes on thin-slice (≤3 mm) multi-detector computerized tomography (CT) scans in the pre-diagnostic phase of pancreatic ductal adenocarcinoma (PDAC) are unknown. It is unclear if identifying imaging changes in this phase will improve PDAC survival beyond lead time. METHODS: From a cohort of 128 subjects (Cohort A) with CT scans done 3-36 months before diagnosis of PDAC we developed a CTgram defining CT Stages (CTS) I through IV in the radiological progression of pre-diagnostic PDAC. We constructed Cohort B of PDAC resected at CTS I and II and compared survival in CTS I and II in Cohort A (n = 22 each; control natural history cohort) vs Cohort B (n = 33 and 72, respectively; early interception cohort). RESULTS: CTs were abnormal in 16% and 85% at 24-36 and 3-6 months respectively, before PDAC diagnosis. The PDAC CTgram stages, findings and median lead times (months) to clinical diagnosis were: CTS I: Abrupt duct cut-off/duct dilatation (-12.8); CTS II: Low density mass confined to pancreas (-9.5), CTS III: Peri-pancreatic infiltration (-5.8), CTS IV: Distant metastases (only at diagnosis). PDAC survival was better in cohort B than in cohort A despite inclusion of lead time in Cohort A: CTS I (36 vs 17.2 months, p = 0.03), CTS II (35.2 vs 15.3 months, p = 0.04). CONCLUSION: Starting 12-18 months before PDAC diagnosis, progressive and increasingly frequent changes occur on CT scans. Resection of PDAC at the time of pre-diagnostic CT changes is likely to provide survival benefit beyond lead time.

Geographic-Level Association of Contemporary Changes in Localized and Metastatic Prostate Cancer Incidence in the Era of Decreasing PSA Screening.

Decreased prostate-specific antigen screening since 2008 has generated much concern, including report of recent increase in metastatic prostate cancer incidence among older men. Although increased metastatic disease was temporally proceeded by decreased screening and decreased localized prostate cancer at diagnosis, it is unclear whether the 2 trends are geographically connected. We therefore used the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database to assess geographic-specific associations between changes in localized (2008-2011) and later changes in metastatic prostate cancer incidence (2012-2015). We examined trends from 200 health-care service areas (HSAs) within SEER 18 registries. While on average for each HSA, localized incidence decreased by 27.4 and metastatic incidence increased by 2.3 per 100 000 men per year, individual HSA-level changes in localized incidence did not correlate with later changes in metastatic disease. Decreased detection of localized disease may not fully explain the recent increase in metastatic disease at diagnosis.

Lead-time bias in esophageal cancer screening in high-risk areas in China.

OBJECTIVE: To investigate what extent lead-time bias is likely to affect endoscopic screening effectiveness for esophageal cancer in the high-risk area in China. METHODS: A screening model based on the epidemiological cancer registry data, yielding a population-level incidence and mortality rates, was carried out to simulate study participants in the high-risk area in China, and investigate the effect of lead-time bias on endoscopic screening with control for length bias. RESULTS: Of 100,000 participants, 6,150 (6.15%) were diagnosed with esophageal squamous dysplasia during the 20-year follow-up period. The estimated lead time ranged from 1.67 to 5.78 years, with a median time of 4.62 years [interquartile range (IQR): 4.07-5.11 years] in the high-risk area in China. Lead-time bias exaggerated screening effectiveness severely, causing more than a 10% overestimation in 5-year cause-specific survival rate and around a 43% reduction in cause-specific hazard ratio. The magnitude of lead-time bias on endoscopic screening for esophageal cancer varied depending on the screening strategies, in which an inverted U-shaped and U-shaped effects were observed in the 5-year cause-specific survival rate and cause-specific hazard ratio respectively concerning a range of ages for primary screening. CONCLUSIONS: Lead-time bias, usually causing an overestimation of screening effectiveness, is an elementary and fundamental issue in cancer screening. Quantification and correction of lead-time bias are essential when evaluating the effectiveness of endoscopic screening in the high-risk area in China.