Point Estimates Research Articles

Parasiticide use and obesity in dogs with sudden acquired retinal degeneration syndrome.

To investigate parasiticide use and describe signalment features in patients with sudden acquired retinal degeneration syndrome (SARDS). Retrospective case-control study of dogs with (n = 71) and without (136) SARDS. Parasiticide use, presentation season, weight, body condition, and signalment were compared between dogs diagnosed with SARDS and the reference population by use of descriptive statistics and logistic regression. Animals with SARDS were at a 5.99 times higher odds of having previously used imidacloprid (95% CI, 1.6 to 22.2; P = .003). However, time of last imidocloprid administration was > 6 years prior to diagnosis in 6 SARDS-affected individuals and 15, 26, or 42 months before diagnosis (n = 1 each). No other class of parasiticide had a significant association with SARDS. Seasonal variation was observed with a negative association identified between incidence of SARDS and tick season (October to January; P < .001). Overweight and obese dogs were 4.42 (95% CI, 1.9 to 10.4) and 4.96 (95% CI, 2.1 to 11.6) times more likely to have SARDS (P ≤ .001). History of polyphagia or weight gain was not associated with an increased likelihood of being overweight or obese within the SARDS-affected population (P > .108). While a statistically significant association was found between imidacloprid use and SARDS, this is unlikely to be clinically significant given the lack of a temporal association, sparse exposure numbers, and low point estimate of the OR. A positive association between being overweight or obese and a diagnosis of SARDS was found independent of polyphagia and weight gain, suggesting that it may be a risk factor for the development of SARDS.

Modification of the association between cold spells and cardiovascular disease by changes in natural gas prices: A nationwide time-series study

BackgroundAlthough the association between low ambient temperature and cardiovascular disease (CVD) is well-established, the effect of winter energy prices on this association remains unknown. AimThis ecological study aimed to investigate whether changes in natural gas prices (NGP), which account for a significant portion of winter energy prices in Korea, affect the association of cold spells with hospital admissions and mortality due to CVD. MethodsData from the National Health Insurance Service and Statistics Korea were used to determine the daily number of hospital admissions and mortality rates associated with CVD. From January 2012 to February 2017, the NGP continually increased by 24.1%. From January 2012 to December 2014 the NGP continually decreased by 32.6% owing to the Korea Gas Corporation’s management decisions, independent of external socioeconomic factors. We investigated the differences in the associations between cold spells and CVD-related outcomes in price-increasing and price-decreasing periods using a Poisson regression with a distributed lag nonlinear model. Cold spells were assessed on two consecutive days at the 5th percentile of the temperature for each region. ResultsThe meteorological factors and air pollution levels were similar between the two periods. The association between cold spells and hospital admissions due to CVD was stronger during the price-increasing period than during the price-decreasing period [ratio of cumulative relative risk (RRR) = 1.71, 95% confidence interval (CI): 1.31–2.22]. The difference in the association with mortality due to CVD between the two periods was not significant, although the point estimate remained >1 (RRR = 1.11, 95% CI: 0.90–1.38). ConclusionsChanges in energy prices may modify the cold spell-related CVD risk, possibly by inducing behavioral changes to manage energy expenditure. Policymakers should take into account the potential public health implications of changes in energy prices, alongside their economic effects.

Patient Enrollment to Industry-Sponsored Versus Federally-Sponsored Cancer Clinical Trials.

The conduct of cancer clinical research in the United States is supported by both private and public sponsors. Industry aims to obtain new drug approvals. Federally-sponsored trials examine a broad set of research questions that are not typically addressed by industry; these trials, which are also more commonly conducted in diverse populations, were recently shown to have contributed to gains of 14 million life-years for patients with cancer. Despite the different mandates, the proportion of patients who might participate in industry-sponsored versus federally-sponsored cancer studies is unknown. We evaluated trial enrollment patterns from 2008 to 2022 using ClinicalTrials.gov data. The ratio of enrollments attributable to industry versus federal sponsors was estimated. A large set of estimates on the basis of different combinations of study characteristics were generated. Point estimates were determined as the mean of combinations and confidence limits by the IQR. Five-year intervals were examined to smooth annual variation. In total, N = 26,080 studies were examined. The estimated enrollment ratio from 2018 to 2022 for all industry-sponsored versus federally-sponsored trials was 8.1 (IQR, 6.2-9.9). For adult trials, the ratio increased from 4.8 (IQR, 4.4-5.3) during 2008-2012 to 9.6 (IQR, 7.4-11.8) during 2018-2022; for trials in children, the ratio increased from 0.7 (IQR, 0.6-0.7) to 2.3 (IQR, 1.8-2.7). Despite increasing cancer incidence, enrollment counts for federally-sponsored trials were flat over the study period. In the United States, there is a growing reliance on industry to conduct cancer clinical research. Underinvestment in federally-sponsored research comes at a cost for both patients and researchers, with lost opportunities for scientific, clinical, and population advances.

A New Modified Confidence Interval Estimator of Location Parameter for Skewed Distribution

A new modified confidence interval has been proposed which incorporates a new point estimator of the location parameter mean for skewed data distribution. Traditionally, the trimmed mean confidence interval estimator (Trm-ci) is a robust method for dealing with the skewness of the underlying data distribution. However, the Trm-ci method trims a certain fraction of endpoint observations to address skewness, which may result in a loss data information. The Students’t confidence interval (t-ci) estimator, while the most efficient estimator at normal models, becomes impractical in a situation where observed data is subject to non-normality due to robustness. In between the two, the median confidence interval estimator (Med-ci) is expected to retain the robustness of Trm-ci and the efficiency of t-ci. The idea behind the proposed new modified confidence interval estimator (Mod-ci) is to consider both the sample mean and sample median simultaneously, while also using end-point information without trimming any observations. As such, the proposed Mod-ci is expected to be as good as or better than other underlying methods regarding robustness and efficiency when dealing with skewed data distributions. In this study, we examine the performance of the new method compared to most commonly used methods through examples and simulating data from skewed distribution with varying degree of skewness. The results of examples and simulations suggest that the proposed method is as good as, or better than other estimators relevant to this study, as measured by estimated coverage probability and the width of associated confidence interval. Therefore, this method is recommended for practical application while dealing with real-life data exhibiting skewness.

Dengue virus genomic surveillance in the applying Wolbachia to eliminate dengue trial reveals genotypic efficacy and disruption of focal transmission.

Release of Aedes aegypti mosquitoes infected with Wolbachia pipientis (wMel strain) is a biocontrol approach against Ae. aegypti-transmitted arboviruses. The Applying Wolbachia to Eliminate Dengue (AWED) cluster-randomised trial was conducted in Yogyakarta, Indonesia in 2018-2020 and provided pivotal evidence for the efficacy of wMel-Ae. aegypti mosquito population replacement in significantly reducing the incidence of virologically-confirmed dengue (VCD) across all four dengue virus (DENV) serotypes. Here, we sequenced the DENV genomes from 318 dengue cases detected in the AWED trial, with the aim of characterising DENV genetic diversity, measuring genotype-specific intervention effects, and inferring DENV transmission dynamics in wMel-treated and untreated areas of Yogyakarta. Phylogenomic analysis of all DENV sequences revealed the co-circulation of five endemic DENV genotypes: DENV-1 genotype I (12.5%) and IV (4.7%), DENV-2 Cosmopolitan (47%), DENV-3 genotype I (8.5%), and DENV-4 genotype II (25.7%), and one recently imported genotype, DENV-4 genotype I (1.6%). The diversity of genotypes detected among AWED trial participants enabled estimation of the genotype-specific protective efficacies of wMel, which were similar (± 10%) to the point estimates of the respective serotype-specific efficacies reported previously. This indicates that wMel afforded protection to all of the six genotypes detected in Yogyakarta. We show that within this substantial overall viral diversity, there was a strong spatial and temporal structure to the DENV genomic relationships, consistent with highly focal DENV transmission around the home in wMel-untreated areas and a near-total disruption of transmission by wMel. These findings can inform long-term monitoring of DENV transmission dynamics in Wolbachia-treated areas including Yogyakarta.

Development of an Optimized Non-Linear Model for Precise Dew Point Estimation in Variable Environmental Conditions

Accurate dew point estimation is crucial for measuring water condensation in various fields such as environmental studies, agronomy, or water harvesting, among others. Despite the numerous models and equations developed over time, including empirical and machine learning approaches, they often involve trade-offs between accuracy, simplicity, and computational cost. A major limitation of the current approaches is the lack of balance among these three factors, limiting their practical applications under diverse conditions. This research addresses these key challenges by developing a new, streamlined equation for dew point estimation. Using the Magnus–Tetens equation, deemed as the most reliable equation, as a benchmark, and by applying a process of non-linear regression fitting and parametric optimization, a new equation was derived. The results demonstrate high accuracy with a streamlined implementation, validated through extensive data and computational simulations. This study highlights the importance of accurate dew point modeling, especially under variable environmental conditions, provides a reliable solution to existing limitations, paving the way for enhanced efficiency in related processes and research endeavors, and offers researchers and practitioners a practical tool for more effective modeling of water condensation phenomena.

Abstract 4114970: Comparing efficacy and safety between pulsed field ablation, cryoballoon ablation and high-power short duration radiofrequency ablation in atrial fibrillation: A systematic review and Network meta-analysis.

Background: Pulsed field ablation (PFA) and high-power short-duration radiofrequency ablation (HPSD) are emerging techniques for treating atrial fibrillation (AF), offering promising results compared to cryoballoon ablation (CBA). This network meta-analysis aims to evaluates the efficacy and safety of PFA, HPSD, and CBA. Method: PubMed, Embase, Cochrane Central Register of Controlled Trials, and EBSCO Information Services were systematically searched for relevant studies until April 2024. The primary outcome is freedom from atrial arrhythmia. A random-effects model was used for data synthesis, and P-scores were employed for outcome ranking. Point estimation (odd ratios) was calculated for comparisons. Results: Fifteen studies were included in our network meta-analysis, involving 5,093 atrial fibrillation patients: 812 (16%), 2,659 (52%), and 1,622 (32%) patients underwent PFA, CBA, and HPSD, respectively. PFA demonstrated the highest efficacy (P-scores 99.3%). Point estimation between PFA and HPSD, and PFA and CBA, were 1.394 (95% CI: 1.047-1.858) and 1.479 (95% CI: 1.134–1.929), respectively. PFA had higher complications compared to HPSD (OR=4.44, 95% CI: 1.405-14.031) and CBA (OR=2.581, 95% CI: 0.992–6.720). HPSD had the shortest fluoroscopic time (P-scores 100%), while CBA had the longest (P-scores 0%). PFA had the shortest procedural time compared to CBA and HPSD with P-scores of 100% 50% and 0%, respectively. Conclusion: PFA showed higher efficacy but higher complication risk than HPSD and CBA. HPSD and CBA demonstrated similar efficacy and safety.

Extending Cochran’s Sample Size Rule to Stratified Simple Random Sampling with Applications to Audit Sampling

Audit samples are selected by businesses, institutions, government agencies, and other organizations to check the accuracy of financial reports and assess the quality of services provided among other reasons. A standard design used in auditing is stratified simple random sampling. Point estimates and confidence intervals for population values are usual products of an audit. Cochran’s sample size rule and its extension by Sugden, Smith, and Jones provided minimum sample size formulas for a simple random sample to ensure effective normal approximation and adequate coverage for nominal 95% confidence intervals of a standardized or Studentized sample mean. The purpose of this paper is to extend Cochran’s rule and establish a formula for the minimum sample size for the normal approximation and the use of traditional one-sided or two-sided confidence intervals to be acceptable for mean estimation in stratified simple random samples. We concentrate on variables that are at least partially continuous. Simulations are used to examine the performance of our sample size formula with a variety of skewed populations based on ones encountered in auditing.

Neural Methods for Amortized Inference

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.

Fetal Gastroschisis: Maternal Epidemiologic Profile and Influence of Possible Risk Factors on Neonatal Outcomes.

The aim of this study was to evaluate the maternal epidemiological profile for gastroschisis as well as to assess whether the presence of meconium at birth was associated with adverse neonatal outcomes. This was a retrospective observational cohort study that included 52 neonates with gastroschisis whose mothers had delivery at our service. The variables observed were the following: 1) socio-demographic data, 2) ultrasonographic data, 3) birth data, and 4) neonatal outcomes. Simple (univariate) and multiple (multivariate) logistic regression models were used to examine the influence of meconium on neonatal outcomes. Mean maternal age and body mass index (BMI) of the pregnant women were 21.9 years and 23.3 kg/m2, respectively. There was a similarity between mixed (50%) and white (48.1%) races, and 27.7% (13) reported to use an oral contraceptive (OC). Mean birth weight was 2,386.9 grams, and 24 of the neonates (46.2%) were premature (<37 weeks). The majority of neonates had neonatal sepsis (40.4%) and 39 were admitted to the neonatal intensive care unit with a median length of stay of 31 days. Forty neonates required mechanical ventilation, with a median duration of 5 days. Primary closure was achieved in 50% (26) of cases. Although not significant and with low precision, the point estimate suggested that the presence of meconium at birth, on average, reduced the chance of primary closure (OR=0.26, CI 95% [0.06; 1.16], p=0.077). Pregnant women with gastroschisis fetuses were more likely to be young, primigravida, with a normal BMI, white or mixed, and with a history of OC use. There was no evidence of an association between the presence of meconium at birth and adverse perinatal outcomes.

Paired piecewise linear regression model with fixed nodes

In this paper we develop a method for constructing a paired regression model in the class of piecewise linear continuous functions with fixed nodes. The concept of linear division of the correlation field, its partial sections and its nodes is introduced into consideration. Using the linear division of the correlation field, a class of piecewise linear functions with fixed nodes is determined. The linear division is revealed during the visual analysis of the correlation field. Using the least squares method, a system of linear equations is compiled to find point estimates of the parameters of the approximating function. With the exception of two unknown angular coefficients (unknown) this system is reduced to a tridiagonal system of equations, the unknowns of which are the nodal values of the approximating function. The tridiagonal system is solved by the run-through method. An algorithm was constructed to demonstrate the operation of the developed method. Its initial data is arrays of the corresponding values of the explanatory and dependent variables, as well as an array of numbers of the right ends of the intervals defining the nodes. An array of fixed nodes is constructed from an array of values of the explanatory variable and an array of numbers of the right ends of the intervals. Next, an array of point estimates of the parameters of the approximating function is constructed. This algorithm is implemented in Python in the form of user-defined functions.

Mental health trajectories across COVID-19 and beyond: South African occupational health surveillance data (2019–2023)

There are inconsistent reports on the impact of the COVID-19 pandemic and its associated social restrictions on mental health, with initial suggestions of an increased prevalence of mental disorders in South Africa. However, the longitudinal trajectories of mental health responses across the COVID-19 pandemic are not clear. The primary aim of this study was to describe the trajectories of self-reported common mental disorders, as determined by repeat administration of clinical screening tools across five consecutive years (from 2019 to 2023). A secondary aim was to consider associations with demographic factors such as gender and age. Point prevalence estimates were obtained from a South African occupational mental health surveillance programme in a stable workplace population. Trajectories of common mental disorders across COVID-19 were determined using the Patient Health Questionnaire-9 and Generalised Anxiety Disorder-7 to identify Major Depressive Disorder and Generalised Anxiety Disorder, respectively. Prevalence estimates for depression and anxiety disorders initially remained stable before increasing towards mid-2021 and returning to pre-COVID-19 estimates by end-2022. Suicidal ideation decreased early into the pandemic but returned to pre-COVID-19 levels by 2021. The study suggests that on a practical level, readily available access to mental health care remains an important mechanism to protect mental health during and after major societal disruptor events. This can further be supported through regular monitoring and through providing high-intensity early intervention for at least 18 to 24 months following such events.

Novel closed-form point estimators for the beta distribution

In this paper, we propose and investigate novel closed-form point estimators for the beta distribution. The estimators of the first type are a modified version of Pearson's method of moments. The underlying idea is to involve the sufficient statistics, i.e., log-moments in the moment estimation equations and solve the mixed type of moment equations simultaneously. The estimators of the second type are based on an approximation to Fisher's likelihood principle. The idea is to solve two score equations derived from the log-likelihood function of generalized beta distributions. Both two resulted estimators are in closed forms, strongly consistent and asymptotically normal. In addition, through theoretical analyses and extensive simulations, the proposed estimators are shown to perform very close to the maximum likelihood estimators in both small and large samples, and they significantly outperform the method of moment estimators.

Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method

Estimating photometric redshifts (photo-z) of quasars is crucial for measuring cosmic distances and monitoring cosmic evolution. While numerous point estimation methods have successfully determined photo-z, they often struggle with the inherently ill-posed nature of the problem and frequently overlook significant morphological features in the probability density functions (pdfs) of photo-z, such as calibration and sharpness. To address these challenges, we introduce a cross-modal contrastive learning probabilistic model that employs adversarial training, contrastive loss functions, and a mixture density network to estimate the pdf of photo-z. This method facilitates the conversion between multiband photometric data attributes, such as magnitude and color, and photometric image features, while extracting features invariant across modalities. We utilize the continuous ranked probability score (CRPS) and the probability integral transform (PIT) as metrics to assess the quality of the pdf. Our approach demonstrates robust performance across various survey bands, image qualities, and redshift distributions. Specifically, in a comprehensive data set from the Sloan Digital Sky Survey and the Wide-field Infrared Survey Explorer (WISE) survey, our probabilistic model achieved a CRPS of 0.1187. Additionally, in a combined data set from SkyMapper and WISE, it reached a CRPS of 0.0035. Our probabilistic model also produced well-calibrated PIT histograms for both data sets, indicating nearly uniform distributions. We further tested our approach in classification tasks within the SkyMapper data set. Despite the absence of u, v, and g bands, it effectively distinguished between quasars, galaxies, and stars with an accuracy of 98.96%. This versatile method can be extended to other scenarios, such as analyzing extended sources like galaxies, across different surveys and varying redshift distributions.

Estimating Ideal Points of British MPs Through Their Social Media Followership

Abstract Ideal points of MPs in the UK House of Commons (HoC) are characteristically difficult to ascertain due to tight party discipline and strategic voting by opposition members. This research note generates left/right ideal point estimates for 591 British MPs sitting in the HoC as of 22/08/2022, ascertained through their social media followership. Specifically, estimates are derived by conducting correspondence analysis (CA) on MP Twitter (X) follower networks, which are subsequently validated against an expert survey, confirming that these estimates have a high degree of between-party (R2 = 0.93) and within-party (Con: r = 0.84; Lab: r = 0.81) accuracy. The informative value of these estimates is then demonstrated by predicting candidate endorsement in the September 2022 Conservative leadership contest, confirming that an MP's ideal point was a statistically significant predictor of candidate endorsement, with Liz Truss drawing support primarily from the further right of the party.

Association of Reperfusion and Procedural Characteristics with Endovascular Thrombectomy Outcomes in Large Core Stroke: Sub-Analysis from the SELECT2 Trial.

From the SELECT2 trial, we evaluated the association between reperfusion status, first-pass effect (near-complete or complete reperfusion [extended thrombolysis in cerebral infarction (eTICI) 2c-3] in 1 pass), procedure time and primary technique (aspiration vs stent-retriever) with functional outcomes in patients receiving EVT across ASPECTS (3 vs 4 vs 5) and core estimate strata (<70 vs ≥70ml, <100 vs ≥100ml, and <150 vs ≥150ml). Of 180 patients who received thrombectomy, 144 (80%) achieved successful reperfusion (eTICI 2b-3) and demonstrated better clinical outcomes (adjusted generalized odds ratios [aGenOR]: 1.48, 95% confidence interval [CI]: 1.01-2.15), compared with unsuccessful reperfusion. Results were consistent across ASPECTS and core estimate strata. Additionally, complete or near-complete reperfusion (eTICI 2c-3) was associated with better functional outcome (aGenOR: 1.99, 95% CI: 1.33-2.97) in patients achieving successful reperfusion. Functional outcome point estimates favored those with first-pass-effect (42 of 167 (25%), aGenOR: 1.46, 95% CI: 0.96-2.24). Longer procedure time was associated with worse modified Rankin scale (mRS) distribution (aGenOR: 0.92, 95% CI: 0.87-0.96, p-value = 0.001 for 10 minutes increment). Aspiration-first technique was used in 43 of 154 (25%) patients and was not associated with higher reperfusion (88% vs 78%, p = 0.18) or better functional outcome (aGenOR: 0.74, 95% CI: 0.50-1.10) as compared with stent-retriever first. Successful reperfusion resulted in improved clinical outcomes in large core patients across baseline ischemic core strata. Near complete or complete reperfusion was further associated with better outcomes, whereas prolonged procedures were associated with worse outcomes. Results were consistent regardless of the technique used. ANN NEUROL 2024.

Heterogeneous Treatment Effects of High-Frequency Oscillatory Ventilation for Acute Respiratory Distress Syndrome: A Post Hoc Analysis of the Oscillation for Acute Respiratory Distress Syndrome Treated Early (OSCILLATE) Trial.

We sought to evaluate whether different subgroups of adults with acute respiratory distress syndrome (ARDS) respond differently to high-frequency oscillatory ventilation (HFOV). The Oscillation for ARDS Treated Early (OSCILLATE) trial was a randomized controlled trial of HFOV vs. conventional ventilation that found an increased risk of in-hospital mortality (primary outcome) with HFOV. In a post hoc analysis, we applied three different approaches to evaluate heterogeneity of treatment effect for in-hospital mortality: 1) subgroup analyses based on baseline Pao2:Fio2 ratio and oxygenation index (OI); 2) a risk-based approach using a multivariable outcome prediction model; and 3) a clustering approach via multivariable latent class analysis. We used multivariable logistic regression models to assess for interaction. Thirty-nine ICUs, five countries. Five hundred forty-eight adults with moderate to severe ARDS. HFOV vs. conventional mechanical ventilation with low tidal volume and higher positive end-expiratory pressure. The effect of HFOV on in-hospital mortality was consistent across categories of Pao2:Fio2 ratio (adjusted odds ratio [aOR], 2.04; 95% CI, 1.32-3.17 and aOR, 1.16; 95% CI, 0.49-2.75 for groups with Pao2:Fio2 above or equal to 80, vs. below 80, respectively; interaction p = 0.23) and OI (aOR, 1.78; 95% CI, 0.67-4.70; aOR, 3.19; 95% CI, 1.44-7.09; aOR, 1.73; 95% CI, 0.82-3.65; and aOR, 1.33; 95% CI, 0.61-2.90 for quartiles of baseline OI, respectively; interaction p = 0.44). Point estimates for the effect of HFOV were consistent across risk categories (aOR, 2.44; 95% CI, 0.40-14.83; aOR, 1.69; 95% CI, 0.75-3.85; and aOR, 2.10; 95% CI, 0.59-7.54 for the lowest, moderate, and highest risk categories, respectively; interaction p = 0.32). Using a clustering approach, point estimates for HFOV were also consistent (cluster 1: aOR, 1.85; 95% CI, 1.15-3.00 and cluster 2: aOR, 1.75; 95% CI, 0.91-3.38; interaction p = 0.75). We did not identify heterogeneity in the effect of HFOV across different subgroups of patients with ARDS.

Model-inferred timing and infectious period of the chickenpox outbreak source

BackgroundIn May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source.MethodsWe conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source’s emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters.ResultsOur models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection.ConclusionThis study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys.Clinical trial numberNot applicable.

A Monte Carlo Investigation of Confidence Intervals for a Nondecreasing Series

We consider the problem of estimating a time series of population means from a series of sample surveys when the means are known to be nondecreasing. We introduce the standard survey estimators of the series of means, which are not guaranteed to be nondecreasing. We employ the Pool Adjacent Violators Algorithm (PAVA) to turn the series of standard survey estimates into a nondecreasing series. We introduce five methods of constructing confidence intervals for the series of non-decreasing means: normal-theory intervals based on the standard survey point estimator of the mean and the Taylor series estimator of its variance; normal-theory intervals based on the nondecreasing PAVA estimator of the mean and a unique jackknife estimator of its variance developed here (jackknife-[Formula: see text]); intervals similar to the aforementioned method but based on Student’s-[Formula: see text] distribution (jackknife-[Formula: see text]); analytical intervals due to Morris; and simultaneous confidence limits due to Korn. We report the results of a Monte Carlo simulation and assess the methods’ performance under various scenarios. Jackknife-[Formula: see text] intervals exhibited coverage probabilities that are near the nominal value. Taylor, jackknife-[Formula: see text], and Korn intervals found coverage below the nominal value while Morris intervals were excessively conservative. Jackknife intervals were much narrower than Taylor intervals.

Cost-effectiveness and cost-utility analysis of a nurse-led, transitional care model to improve care coordination for patients with cardiovascular diseases: results from the "Cardiolotse" study.

To assess the 12-month cost-effectiveness of the nurse-led transitional care program "Cardiolotse" (CL) for patients with cardiovascular diseases compared to usual care (UC). A cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) were conducted from the perspective of statutory health insurance (SHI), covering a time horizon of 12 months. Analyzed outcomes included the number of rehospitalizations and health-related quality of life (HRQoL). Total costs comprised program costs and the utilization of healthcare resources. Point estimates are presented as incremental cost-effectiveness ratios (ICERs) and incremental cost-utility ratios (ICURs). Sensitivity and subgroup analyses were conducted to illustrate uncertainty and provide insights into the impact mechanisms of the CL program. The study population consisted of 2550 patients, with 1256 allocated to the intervention group and 1294 to the control group. Patients who received support from CLs experienced fewer rehospitalizations and lower inpatient costs from an SHI perspective, compared to the UC group. HRQoL assessments indicated higher utility values for CL patients at the 12-month follow-up. Total program costs amounted to €1454.65 per patient. The CEA and CUA demonstrate that the CL program is dominant compared to UC from the SHI perspective. Our study shows that the CL program not only reduces the number of rehospitalizations and costs but increases HRQoL, resulting in a dominant ICER and ICUR. Further research is necessary to evaluate longer periods of time, different levels of care intensity, and perspectives of different healthcare stakeholders. German Clinical Trial Register DRKS00020424, 2020-06-18, retrospectively registered.