Estimates of the lead time in screening for bladder cancer

Some studies have suggested a survival benefit from early treatment of bladder cancer (BC). This benefit may be due in part to a "lead-time" bias (LT), i.e., the time interval between the detection of BC in asymptomatic individuals and the development of symptoms ("backward prolongation of survival"). To estimate the LT of BC, it was assumed that LT corresponds to the ratio between the prevalence of pre-symptomatic BC and the incidence of symptomatic BC. Data on the prevalence of pre-symptomatic BC were derived from published screening studies. Data on the annual incidence of symptomatic BC at the age and gender of the study populations were derived from national registries in the countries in the years in which the screening studies were conducted. The ratios of the prevalence of presymptomatic BC to the incidence of symptomatic BC ranged from 3.3 to 12.1 years when derived from screening for microhematuria, and from 1.8 to 5.3 years when derived from screening for urine cytology and cell markers. The estimates of the LT of BC derived from the ratios between its prevalence in asymptomatic persons and its incidence in the corresponding population were consistent with those previously reported in retrospective and prospective cohort studies. Since these estimates may account for the survival benefit from early treatment of BC, the gain of screening for BC remains uncertain and should be confirmed by controlled randomized trials.

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.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Factors associated with non-attendance at appointments in the gastroenterology endoscopy unit: a retrospective cohort study

Background and Aims

Gastrointestinal (GI) endoscopy is a limited health resource because of a scarcity of qualified personnel and limited availability of equipment. Non-adherence to endoscopy appointments therefore wastes healthcare resources and may compromise the early detection and treatment of GI diseases. We aimed to identify factors affecting non-attendance at scheduled appointments for GI endoscopy and thus improve GI healthcare outcomes.

Methods

This was a single-center retrospective cohort study performed at a tertiary hospital gastroenterology endoscopy unit, 12 months before and 12 months after the start of the COVID-19 pandemic. We used multiple logistic regression analysis to identify variables associated with non-attendance at scheduled appointments.

Results

Overall, 5,938 appointments were analyzed, and the non-attendance rate was 18.3% (1,088). The non-attendance rate fell significantly during the pandemic (22.6% vs. 11.6%, p < 0.001). Multivariable regression analysis identified the absence of deep sedation (OR: 3.253, 95% CI [2.386-4.435]; p < 0.001), a referral from a physician other than a gastroenterologist (OR: 1.891, 95% CI [1.630-2.193]; p < 0.001), a longer lead time (OR: 1.006, 95% CI [1.004-1.008]; p < 0.001), and female gender (OR: 1.187, 95% CI [1.033-1.363]; p = 0.015) as associated with appointment non-attendance.

Conclusions

Female patients, those undergoing endoscopic procedures without deep sedation, those referred by physicians other than gastroenterologists, and with longer lead time were less likely to adhere to appointments. Precautions should be directed at patients with one or more of these risk factors, and for those scheduled for screening procedures during the COVID-19 pandemic.

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.

The effects of takeover request lead time on drivers' situation awareness for manually exiting from freeways: A web-based study on level 3 automated vehicles

Conditional automation systems allow drivers to turn their attention away from the driving task in certain scenarios but still require drivers to gain situation awareness (SA) upon a takeover request (ToR) and resume manual control when the system is unable to handle the upcoming situation. Unlike time-critical takeover situations in which drivers must respond within a relatively short time frame, the ToRs for non-critical events such as exiting from a freeway can be scheduled way ahead of time. It is unknown how the ToR lead time affects driver SA for resuming manual control and when to send the ToR is most appropriate in non-critical takeover events. The present study conducted a web-based, supervised experiment with 31 participants using conditional automation systems in freeway existing scenarios while playing a mobile game. Each participant experienced 12 trials with different ToR lead times (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s) for exiting from freeways in a randomized order. Driver SA was measured by using a freeze probe technique in each trial when the participant pressed the spacebar on the laptop to simulate the takeover action. Results revealed a positive effect of longer ToR lead times on driver SA for resuming control to exit from freeways and the effect leveled off at the lead time of 16-30 s. The participants tended to postpone their takeover actions further when they were given a longer ToR lead time and it did not level off up to 60 s. Nevertheless, not all drivers waited till the last moment to take over AVs even though they did not get sufficient SA. The ToR lead time of 16-30 s was recommended for better SA; and it could be narrowed down to 25-30 s if considering the subjective evaluations on takeover readiness, workload, and trust. The findings provide implications for the future design of conditional automation systems used for freeway driving.

When to initiate cancer screening exam?

A probability method is developed to decide when to initiate cancer screening for asymptomatic individuals. The probability of incidence is a function of screening sensitivity, time duration in the disease-free state and sojourn time in the preclinical state; and it is monotonically increasing as time increases, given a person's current age. So a unique solution of the first screening time can be found by limiting this probability to a small value, such as 10% or 20%. That is, with 90% or 80% probability, one will not be a clinical incident case before the first exam. After this age is found, we can further estimate the lead time distribution and probability of over-diagnosis if one would be diagnosed with cancer at the first exam. Simulations were carried out under different scenarios; and the method was applied to two heavy smoker cohorts in the National Lung Screening Trial using low-dose computerized tomography. The method is applicable to other kinds of cancer screening. The predictive information can be used by physicians or individuals at risk to make informed decisions on when to initiate screening.

Takeover requests for automated driving: The effects of signal direction, lead time, and modality on takeover performance

Vehicle-to-driver takeover will still be needed in semi-autonomous vehicles. Due to the complexity of the takeover process, it is important to develop interfaces to support good takeover performance. Multimodal displays have been proposed as a candidate for the design of takeover requests (TORs), but many questions remain unanswered regarding the effectiveness of this approach. This study investigated the effects of takeover signal direction (ipsilateral vs. contralateral), lead time (4 vs. 7 s), and modality (uni-, bi-, and trimodal combinations of visual, auditory, and tactile signals) on automated vehicle takeover performance. Twenty-four participants rode in a simulated SAE Level 3 vehicle and performed a series of takeover tasks when presented with a TOR. Overall, single and multimodal signals with a tactile component were correlated with the faster takeover and information processing times, and were perceived as most useful. Ipsilateral signals showed a marginally significant benefit to takeover times compared to contralateral signals. Finally, a shorter lead time was associated with faster takeover times, but also poorer takeover quality. Findings from this study can inform the design of in-vehicle information and warning systems for next-generation transportation.

Actions taken affecting lead time in the care pathway for low-priority patients with a suspected stroke: A critical incident study

BACKGROUND

The time delay from alerted ambulance to arrival at the stroke unit is crucial for patients suffering a suspected stroke. This is a recognized problem why additional explorative knowledge regarding actions taken are needed.

AIM

To explore actions taken by nurses that affect lead times in the care pathway from the alerted ambulance to the stroke unit, for low-priority patients suffering a suspected stroke.

METHOD

The design of the study was explorative and descriptive and used a qualitative approach based on Critical Incident Technique (CIT). Twenty-two nurses involved in the stroke care pathway at an university hospital in western Sweden were interviewed about their actions that affected the lead time.

RESULTS

Actions undertaken affected lead time in the stroke care pathway for low-priority patients related to "promoting the care chain process" and "taking control of the situation".

CONCLUSIONS

The staff within all parts of the care pathway affected the lead time, individually as well as via interaction between departments. This calls for the need of further collaboration and consensus concerning how to facilitate a smooth care pathway.

Experienced critical incidents affecting lead-times in the stroke care chain for low-priority patients - A qualitative study with critical incident technique

BACKGROUND

The time elapsing from the alarm being raised to arrival at the stroke unit is crucial for patients suffering a conceivable stroke. More knowledge is needed about critical incidents-both favourable and unfavourable-affecting the lead time in the care chain.

AIM

To explore favourable and unfavourable critical incidents (CIs), affecting lead times in the care chain from the alerted ambulance to the stroke unit, as experienced by nurses, for low-priority patients suffering a conceivable acute stroke.

METHOD

The study had an explorative descriptive design using a qualitative approach of Flanagan's Critical Incident Technique (CIT). Twenty-two nurses involved in the stroke care chain at a hospital in western Sweden were interviewed about their experiences regarding CIs influencing the lead time for low-priority patients.

RESULTS

There were 363 CIs identified related to human interactions and organizational structures. The types of CIs varied depending on where in the care chain they were experienced. Both favourable and unfavourable factors had an impact on the length of the lead time from alerted ambulance to stroke unit for lowpriority patients. Furthermore, CIs occurring both early and later in the care chain affected the whole process.

CONCLUSIONS

There is a need for greater understanding within each interrelated department in the care chain about how CIs at one part of the care chain have an impact on another.

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.

Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R²) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.

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.

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.

Infantile spasms: Etiology, lead time and treatment response in a resource limited setting

•

Neonatal hypoglycemic brain injury was the commonest cause of Infantile Spasms (IS).

•

Comprehensive genetic evaluation was performed in presumed genetic IS patients.

•

Molecular diagnosis was achieved in 44% of presumed genetic patients.

•

Longer lead time to treatment was significantly associated with resistant spasms.

This study explores the etiology and lead time to treatment for infantile spasm (IS) patients and their effect on treatment responsiveness, in a limited resource setting. Patients with IS onset age ≤12 months’, seen over 3 years were recruited retrospectively. Clinical information, neuroimaging and genetic results retrieved. Patients categorized into three primary etiological groups: Structural (including Structural Genetic), Genetic, and Unknown. The effect of etiology and lead time from IS onset to initiating appropriate treatment on spasm resolution, evaluated. Total 113 patients were eligible. Mean IS onset age was 6.86(±4.25) months (M: F 3.3:1). Patients were grouped into: Structural 85, Genetic 11 and Unknown 17. Etiology was ascertained in 94/113 (83.1%) with neonatal hypoglycemic brain injury (NHBI) being the most common (40/113, 36%). A genetic etiology identified in 17 (including 6 Structural Genetic, of which five had Tuberous Sclerosis). Structural group was less likely to be treatment resistant (p = 0.013, OR 0.30 [0.12–0.76]). Median treatment lead time – 60 days. Longer lead time to treatment was significantly associated with resistant spasms (χ² for trend = 10.0, p = 0.0015). NHBI was the commonest underlying cause of IS. There was significant time lag to initiating appropriate treatment, affecting treatment responsiveness.

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.

Vaccine stock management in primary health care facilities in OR Tambo District, Eastern Cape, South Africa

BACKGROUND

Poor stock management has been identified as one of the causes of vaccine stock-outs in health facilities. This study assessed the occurrence of vaccine stock-outs and vaccine stock management practices in primary health care facilities in OR Tambo district of the Eastern Cape province, South Africa.

METHODS

Descriptive cross-sectional study design was used in this study to assess both the stock management practices and the availability of vaccines in the facilities. The study was conducted in 64 primary health care facilities across all sub-districts in OR Tambo. Data were collected using a questionnaire administered by the researcher, record checks and through observation. The occurrence of stock-outs of six tracer vaccines on the day of the visit and in the preceding 24 months were assessed. The data were captured into REDCap tool and analysed using STATA version 14.

RESULTS

Most standard stock management procedures were not adhered to, as these procedures were mostly handled by health care workers who either required formal training or refresher training on vaccine management. Cold chain capacity was not adequate and some vaccines were exposed to freezing. Both stock cards and the stock visibility solution (SVS) device were used in all the facilities for vaccine stock management. Less than half of the facilities visited 27 (44%) filled their stock cards regularly. Delays in receiving supplies from the pharmaceutical depot were commonly reported by facilities, which contributed to stock-outs. A total of 49 (77%) health facilities had at least one stockout for at least one vaccine on the day of the visit. Furthermore, BCG and OPV were the most affected vaccines in 37 (58%) and 28 (44%) of facilities, respectively.

CONCLUSION

Interventions for improving vaccine availability should be considered, especially those targeting human resources and the entire stock management system.

Development and evaluation of a real-time forecasting framework for daily water quality forecasts for Lake Chaohu to Lead time of six days

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu.

Intravenous fluid delivery time improvement: application of cross-docking system

PURPOSE

The cost of pharmaceutical supply chain due to drug waste is one of the current major issues in health care. Drug waste associated with intravenous (IV) fluid form of medication is one of the crucial issues for many pharmacies. The purpose of this paper is to apply a cross-docking model to minimize the IV delivery lead time to reduce drug waste by scheduling staff in a local hospital's inpatient pharmacy.

DESIGN/METHODOLOGY/APPROACH

A mixed integer linear programming model is applied to the IV delivery system of a hospital. The parameters are selected based on the observations made in the inpatient pharmacy.

FINDINGS

The result implies that cross-docking approach can be effectively applied to IV delivery system. In fact, the cross-docking optimization model employed in this case study reduces the IV delivery completion time of the inpatient pharmacy by 41 percent.

RESEARCH LIMITATIONS/IMPLICATIONS

The scope of this research is limited to the activities performed after IV preparation.

PRACTICAL IMPLICATIONS

The application of cross-docking system in staff scheduling will be beneficial for health care organizations that aim to minimize medication waste.

ORIGINALITY/VALUE

The prime value of this study lies in the introduction of a cross-docking concept in an internal hospital ordering process. Cross-docking models are widely used in general supply chain systems; however, their application for specific activities inside hospitals is the novelty of this study, which can fill the research gap in terms of drug waste management within the inpatient pharmacy.

[Discrepancies and overdiagnosis in breast cancer organized screening. A "methodology" systematic review]

BACKGROUND

The risk-benefit ratio of breast cancer organized screening is the focus of much scientific controversy, especially about overdiagnosis. The aim of this study was to relate methodological discrepancies to variations in rates of overdiagnosis to help build future decision aids and to better communicate with patients.

METHODS

A systematic review of methodology was conducted by two investigators who searched Medline and Cochrane databases from 01/01/2004 to 12/31/2016. Results were restricted to randomized controlled trials (RCTs) and observational studies in French or English that examined the question of the overdiagnosis computation.

RESULTS

Twenty-three observational studies and four RCTs were analyzed. The methods used comparisons of annual or cumulative incidence rates (age-cohort model) in populations invited to screen versus non-invited populations. Lead time and ductal carcinoma in situ (DCIS) were often taken into account. Some studies used statistical modeling based on the natural history of breast cancer and gradual screening implementation. Adjustments for lead time lowered the rate of overdiagnosis. Rate discrepancies, ranging from 1 to 15 % for some authors and around 30 % for others, could be explained by the hypotheses accepted concerning very slow growing tumors or tumors that regress spontaneously.

CONCLUSION

Apparently, research has to be centered on the natural history of breast cancer in order to provide responses concerning the questions raised by the overdiagnosis controversy.

Effects of lead time of verbal collision warning messages on driving behavior in connected vehicle settings

INTRODUCTION

Under the connected vehicle environment, vehicles will be able to exchange traffic information with roadway infrastructure and other vehicles. With such information, collision warning systems (CWSs) will be able to warn drivers with potentially hazardous situations within or out of sight and reduce collision accidents. The lead time of warning messages is a crucial factor in determining the effectiveness of CWSs in the prevention of traffic accidents. Accordingly, it is necessary to understand the effects of lead time on driving behaviors and explore the optimal lead time in various collision scenarios.

METHODS

The present driving simulator experiment studied the effects of controlled lead time at 16 levels (predetermined time headway from the subject vehicle to the collision location when the warning message broadcasted to a driver) on driving behaviors in various collision scenarios.

RESULTS

Maximum effectiveness of warning messages was achieved when the controlled lead time was within the range of 5s to 8s. Specifically, the controlled lead time ranging from 4s to 8s led to the optimal safety benefit; and the controlled lead time ranging from 5s to 8s led to more gradual braking and shorter reaction time. Furthermore, a trapezoidal distribution of warning effectiveness was found by building a statistic model using curve estimation considering lead time, lifetime driving experience, and driving speed.

CONCLUSIONS

The results indicated that the controlled lead time significantly affected driver performance.

PRACTICAL APPLICATIONS

The findings have implications for the design of collision warning systems.

Overdiagnosis associated with breast cancer screening: A simulation study to compare lead-time adjustment methods

OBJECTIVE

Estimating overdiagnosis associated with breast cancer screening may use annual incidence rates of cancer. We simulated populations invited to screening programmes to assess two lead-time adjustment methods.

METHODS

Overdiagnosis estimates were computed using the compensatory drop method, which considered the decrease in incidence of cancers among older age groups no longer offered screening, and the method based on the decrease in incidence of late-stage cancers.

RESULTS

The true value of overdiagnosis was 0% in all the data sets simulated. The compensatory drop method yielded an overdiagnosis estimate of -0.1% (95% credibility interval -0.5% to 0.5%) when participation rates among the population and risk of cancers were constant. However, if participation rates increased with calendar year as well as risk of cancer with birth cohorts, the overdiagnosis estimated was 11.0% (10.5-11.6%). Using the method based on the incidence of early- and late-stage cancers, overdiagnosis estimates were 8.9% (8.5-9.3%) and 17.6% (17.4-17.9%) when participation rates and risks of cancer were constant or increased with time, respectively.

CONCLUSION

Adjustment for lead time based on the compensatory drop method is accurate only when participation rates and risks of cancer remain constant, whereas the adjustment method based on the incidence of early- and late-stage cancers results in overestimating overdiagnosis regardless of stability of participation rates and breast cancer risk.

Principles of Cancer Screening

Cancer screening has long been an important component of the struggle to reduce the burden of morbidity and mortality from cancer. Notwithstanding this history, many aspects of cancer screening remain poorly understood. This article presents a summary of basic principles of cancer screening that are relevant for researchers, clinicians, and public health officials alike.

Is symptom-oriented follow-up still up to date?

The main objective of following patients after the primary treatment of breast cancer is the detection of potentially curable events, particularly the detection of local recurrences and contralateral breast cancer. Additionally, medical counseling on therapies, psychosocial aspects, side effects of therapies, and lifestyle interventions is important to improve the quality of life. There is an ongoing discussion about whether early detection of asymptomatic metastasis could improve the course of disease. Today, the follow-up is still symptom-orientated. Intensified imaging and laboratory check-ups have not been beneficial for the patients' survival. A follow-up in the first 2-3 years is recommended every 3 months. Because of the decreasing incidence of recurrence from year 4, 6-monthly screening intervals are recommended. The screening should include a history, physical examination, and a consultation. Routine diagnostic imaging - except for mammography/ultrasound - is not indicated in asymptomatic patients. Innovative therapies for patients with metastatic breast cancer have been introduced. Therefore, measures of an intensified follow-up could change in the future as novel endocrine combination or targeted therapies in molecular subtypes could significantly improve the survival in early detected metastasis. In the future, more individualized follow-up programs are conceivable. However, this idea is so far not supported by the available data.

Bias in breast cancer research in the screening era

Screening aims to detect breast cancer at an earlier stage than would occur if symptoms developed. The characteristics of breast cancer that are detectable at screening depend on both the physical properties of the screening test and specific anatomical features of breast cancer. As a result, breast cancer detected by screening is a select subset of all breast cancer existing in the population. Therefore, biomedical, clinical and epidemiological research into breast cancer using populations with access to screening can result in major bias. The biases, with examples, are explained.

Bayesian lead time estimation for the Johns Hopkins Lung Project data

Problem statement: Lung cancer screening using X-rays has been controversial for many years. A major concern is whether lung cancer screening really brings any survival benefits, which depends on effective treatment after early detection. The problem was analyzed from a different point of view and estimates were presented of the projected lead time for participants in a lung cancer screening program using the Johns Hopkins Lung Project (JHLP) data.

Method: The newly developed method of lead time estimation was applied where the lifetime T was treated as a random variable rather than a fixed value, resulting in the number of future screenings for a given individual is a random variable. Using the actuarial life table available from the United States Social Security Administration, the lifetime distribution was first obtained, then the lead time distribution was projected using the JHLP data.

Results: The data analysis with the JHLP data shows that, for a male heavy smoker with initial screening ages at 50, 60, and 70, the probability of no-early-detection with semiannual screens will be 32.16%, 32.45%, and 33.17%, respectively; while the mean lead time is 1.36, 1.33 and 1.23 years. The probability of no-early-detection increases monotonically when the screening interval increases, and it increases slightly as the initial age increases for the same screening interval. The mean lead time and its standard error decrease when the screening interval increases for all age groups, and both decrease when initial age increases with the same screening interval.

Conclusion: The overall mean lead time estimated with a random lifetime T is slightly less than that with a fixed value of T. This result is hoped to be of benefit to improve current screening programs.