The limited skill of seasonal climate predictions in some regions of the Southern African Development Community (SADC) restricts their potential application to the development of climate services. This study explores the feasibility of improving the quality of these predictions by using the observed relationship between Essential Climate Variables (ECVs) and large-scale teleconnection indices, namely Niño3.4, the Atlantic Niño and the Indian Ocean Dipole. The underlying hypothesis is that, for certain areas, the empirical observed teleconnections could improve the predictions offered by the seasonal forecasting systems. This is achieved by implementing linear regression models between each index and ECV, for up to 12 months into the past, and for each season and grid point. After obtaining the index-derived estimates of the variables, the correlation coefficients and fair Ranked Probability Skill Scores (fRPSS) are computed and compared to those of the ECMWF SEAS5 (SEAS5) predictions for different lead times. The results show that 10-25 % of the entire domain exhibits improved correlations for the index-derived precipitation in all seasons. In the case of temperature, though, higher correlations could be observed only in six seasons (and solely for Niño3.4). Regarding fRPSS, up to 7 % of the entire area shows an improvement when using Niño3.4 to estimate temperature (in four seasons). Conversely, for precipitation there is no detected enhancement. In future work, it would be worth investigating whether a combined multi-index regression can further raise the observed increase in performance.

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.

(1) Background: Screen-detected breast cancer patients tend to have better survival than patients diagnosed with symptomatic cancer. The main driver of improved survival in screen-detected cancer is detection at earlier stage. An important bias is introduced by lead time, i.e., the time span by which the diagnosis has been advanced by screening. We examine whether there is a remaining survival difference that could be attributable to mode of detection, for example, because of higher quality of care. (2) Methods: Women with a breast cancer (BC) diagnosis in 2000-2022 were included from a population-based cancer registry from Schleswig-Holstein, Germany, which also registers the mode of cancer detection. Mammography screening was available from 2005 onwards. We compared the survival for BC detected by screening with symptomatic BC detection using Kaplan-Meier, unadjusted Cox regressions, and Cox regressions adjusted for age, grading, and UICC stage. Correction for lead time bias was carried out by assuming an exponential distribution of the period during which the tumor is asymptomatic but screen-detectable (sojourn time). We used a common estimate and two recently published estimates of sojourn times. (3) Results: The analysis included 32,169 women. Survival for symptomatic BC was lower than for screen-detected BC (hazard ratio (HR): 0.23, 95% confidence interval (CI): 0.21-0.25). Adjustment for prognostic factors and lead time bias with the commonly used sojourn time resulted in an HR of 0.84 (CI: 0.75-0.94). Using different sojourn times resulted in an HR of 0.73 to 0.90. (4) Conclusions: Survival for symptomatic BC was only one quarter of screen-detected tumors, which is obviously biased. After adjustment for lead-time bias and prognostic variables, including UICC stage, survival was 27% to 10% better for screen-detected BC, which might be attributed to BC screening. Although this result fits quite well with published results for other countries with BC screening, further sources for residual confounding (e.g., self-selection) cannot be ruled out.

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.

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).
CONCLUSIONS: 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.

BACKGROUND: In the Netherlands, the COVID-19 pandemic resulted in a temporary halt of population screening for cancer and limited hospital capacity for non-COVID care. We aimed to investigate the impact of the pandemic on the in-hospital diagnostic pathway of breast cancer (BC) and colorectal cancer (CRC).
METHODS: 71,159 BC and 48,900 CRC patients were selected from the Netherlands Cancer Registry. Patients, diagnosed between January 2020 and July 2021, were divided into six periods and compared to the average of patients diagnosed in the same periods in 2017-2019. Diagnostic procedures performed were analysed using logistic regression. Lead time of the diagnostic pathway was analysed using Cox regression. Analyses were stratified for cancer type and corrected for age, sex (only CRC), stage and region.
RESULTS: For BC, less mammograms were performed during the first recovery period in 2020. More PET-CTs were performed during the first peak, first recovery and third peak period. For CRC, less ultrasounds and more CT scans and MRIs were performed during the first peak. Lead time decreased the most during the first peak by 2 days (BC) and 8 days (CRC). Significantly fewer patients, mainly in lower stages, were diagnosed with BC (-47%) and CRC (-36%) during the first peak.
CONCLUSIONS: Significant impact of the COVID-19 pandemic was found on the diagnostic pathway, mainly during the first peak. In 2021, care returned to the same standards as before the pandemic. Long-term effects on patient outcomes are not known yet and will be the subject of future research.

OBJECTIVE: 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.
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.
CONCLUSIONS: 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.

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.

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.

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.
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.
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.
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.