Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence

Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening-behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way. We show that decreased adherence leads to reduced efficacy of screening programs, quantified here using elements of the lead time distribution (i.e., the time between screening diagnosis and when diagnosis would have occurred clinically in the absence of screening). Under the assumption of an inverse relationship between prescribed screening frequency and individual adherence, we show that the optimal screening frequency generally decreases with increasing levels of non-adherence. We apply this model to an example in breast cancer screening, demonstrating how accounting for imperfect adherence affects the recommended screening frequency.

A two-time-scale point process model of water main breaks for infrastructure asset management

Deterioration modelling has been a bottlenecking step towards risk-informed asset management of municipal water distribution networks. To close the gap, we proposed a two-time-scale (TTS) point process model on a pipe level for modelling and prediction of water main breaks. This paper presents the characterization, statistical parameter estimation, probabilistic features, and application of the model. Combining Poisson and renewal models into one, the proposed TTS process is characterized by a conditional intensity function of two time variables-one in a pipe clock for overall pipe aging and the other in a repair clock for local renewal. As a result, different aging patterns including the complicated bathtub-type behaviour can be modelled. A novel statistical method that combines data augmentation and Markov Chain Monte Carlo was developed for model estimation to deal with partially missing event histories. A case study using real-life data collected from a regional municipality in Canada was presented to illustrate the application of the proposed model. The modelling process ranging from model estimation, verification, validation, and updating to application in asset management was thoroughly demonstrated. This study also demonstrated that one must use the full distributions of the parameters to obtain an unbiased prediction of mean number of water main breaks. The proposed model was also compared with the Poisson process model in terms of break intensity, survival probability, mean cumulative number of breaks, and mean annual number of breaks. The implications of the different results to asset management were carefully discussed as well. Last, the ability of the proposed model to capture the maintenance effectiveness of pipe repair was proven. This work represents a solid advancement towards holistic assessment of the aging risk of a municipal water distribution network.

The mTORC1 signaling modulated by intracellular C3 activation in Paneth cells promotes intestinal epithelial regeneration during acute injury

Complement activation is associated with regional inflammation during acute gastrointestinal injury (AGI). This study is designed to explore how intracellular C3 activation in Paneth cells (PCs) affects regeneration of intestinal epithelium during AGI. AGI was induced in wildtype C57BL/6 mice, with sham operation employed as control. Exogenous C3 (1 mg, I.P.) was applied at 6 h post-surgery. Intestinal crypts harvested from ileum were cultured with presence or absence of C3 (20 μg/ml), with small interfering RNA against BST1 and complement activation inhibitor selectively applied in vitro. The intestinal integrity, percentage of PCs and intestinal stem cells (ISCs) were evaluated. Importantly, cADPR, C3 fragments, and S6-related proteins were detected in PCs to inspect the mammalian target of rapamycin complex 1 (mTORC1) signaling. AGI caused breakdown of intestinal mucosa integrity and regional inflammation. Exogenous C3 by itself failed to promote the growth of intestinal epithelium, but distinctly enhanced the activity of PCs via intracellular activation, which subsequently supported the expansion of ISCs inside of intestinal crypts. Inhibition of C3 activation was associated with decreased expressions of S6, S6K1 and cADPR, with blocking BST1 found to depress cADPR only. Collectively, these data confirmed intracellular activation of C3 in PCs enhanced expansion of ISCs in response to acute injury. The mTORC1 signaling pathway in PCs contributed to this crosstalk during exogenous C3 treatment.

Web renewal counting processes and their applications in insurance

This paper investigates a nonstandard renewal counting process with dependent inter-arrival times-web renewal process. Several limit properties, including the tail of the exponential moment which is a crucial condition in many situations, are obtained. Then the results are applied in insurance to derive precise large deviations and moderate deviation formulas for the aggregate amount of claims.

Regularity of a renewal process estimated from binary data

Assessment of the regularity of a sequence of events over time is important for clinical decision-making as well as informing public health policy. Our motivating example involves determining the effect of an intervention on the regularity of HIV self-testing behavior among high-risk individuals when exact self-testing times are not recorded. Assuming that these unobserved testing times follow a renewal process, the goals of this work are to develop suitable methods for estimating its distributional parameters when only the presence or absence of at least one event per subject in each of several observation windows is recorded. We propose two approaches to estimation and inference: a likelihood-based discrete survival model using only time to first event; and a potentially more efficient quasi-likelihood approach based on the forward recurrence time distribution using all available data. Regularity is quantified and estimated by the coefficient of variation (CV) of the interevent time distribution. Focusing on the gamma renewal process, where the shape parameter of the corresponding interevent time distribution has a monotone relationship with its CV, we conduct simulation studies to evaluate the performance of the proposed methods. We then apply them to our motivating example, concluding that the use of text message reminders significantly improves the regularity of self-testing, but not its frequency. A discussion on interesting directions for further research is provided.

Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia

The Canadian Study of Health and Aging (CSHA) employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. The prevalent cohort sampling scheme favors individuals who survive longer. Thus, the observed survival times are subject to length bias. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. This article considers semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Moreover, the asymptotic properties of the semiparametric maximum likelihood estimator are not well-studied. Motivated by the composite likelihood method (Besag 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software. When applied to the CSHA data, the proposed method estimates a significant difference in survival between the vascular dementia group and the possible Alzheimer's disease group, while the partial likelihood method for left-truncated and right-censored data yields a greater standard error and a 95% confidence interval covering 0, thus highlighting the practical value of employing a more efficient methodology. To check the assumption of stable disease for the CSHA data, we also present new graphical and numerical tests in the article. The R code used to obtain the maximum composite partial likelihood estimator for the CSHA data is available in the online Supplementary Material, posted on the journal web site.