An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada.

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.

Assessing dependence between frequency and severity through shared random effects.

Research on the occurrence and the final size of wildland fires typically models these two events as two separate processes. In this work, we develop and apply a compound process framework for jointly modelling the frequency and the severity of wildland fires. Separate modelling structures for the frequency and the size of fires are linked through a shared random effect. This allows us to fit an appropriate model for frequency and an appropriate model for size of fires while still having a method to estimate the direction and strength of the relationship (e.g., whether days with more fires are associated with days with large fires). The joint estimation of this random effect shares information between the models without assuming a causal structure. We explore spatial and temporal autocorrelation of the random effects to identify additional variation not explained by the inclusion of weather related covariates. The dependence between frequency and size of lightning-caused fires is found to be negative, indicating that an increase in the number of expected fires is associated with a decrease in the expected size of those fires, possibly due to the rainy conditions necessary for an increase in lightning. Person-caused fires were found to be positively dependent, possibly due to dry weather increasing human activity as well as the amount of dry few. For a test for independence, we perform a power study and find that simply checking whether zero is in the credible interval of the posterior of the linking parameter is as powerful as more complicated tests.

A multi-state model for the analysis of changes in cognitive scores over a fixed time interval.

In this article, we present the novel approach of using a multi-state model to describe longitudinal changes in cognitive test scores. Scores are modelled according to a truncated Poisson distribution, conditional on survival to a fixed endpoint, with the Poisson mean dependent upon the baseline score and covariates. The model provides a unified treatment of the distribution of cognitive scores, taking into account baseline scores and survival. It offers a simple framework for the simultaneous estimation of the effect of covariates modulating these distributions, over different baseline scores. A distinguishing feature is that this approach permits estimation of the probabilities of transitions in different directions: improvements, declines and death. The basic model is characterised by four parameters, two of which represent cognitive transitions in survivors, both for individuals with no cognitive errors at baseline and for those with non-zero errors, within the range of test scores. The two other parameters represent corresponding likelihoods of death. The model is applied to an analysis of data from the Canadian Study of Health and Aging (1991-2001) to identify the risk of death, and of changes in cognitive function as assessed by errors in the Modified Mini-Mental State Examination. The model performance is compared with more conventional approaches, such as multivariate linear and polytomous regressions. This model can also be readily applied to a wide variety of other cognitive test scores and phenomena which change with age.

Medroxyprogesterone and conjugated oestrogen are equivalent for hot flushes: a 1-year randomized double-blind trial following premenopausal ovariectomy.

Oestrogen therapy is the gold standard treatment for hot flushes/night sweats, but it and oestrogen/progestin are not suitable for all women. MPA (medroxyprogesterone acetate) reduces hot flushes, but its effectiveness compared with oestrogen is unknown. In the present study, oral oestrogen [CEE (conjugated equine oestrogen)] and MPA were compared for their effects on hot flushes in a planned analysis of a secondary outcome for a 1-year randomized double-blind parallel group controlled trial in an urban academic medical centre. Participants were healthy menstruating women prior to hysterectomy/ovariectomy for benign disease. A total of 41 women {age, 45 (5) years [value is mean (S.D.)]} were enrolled; 38 women were included in this analysis of daily identical capsules containing CEE (0.6 mg/day) or MPA (10 mg/day). Demographic variables did not differ at baseline. Daily data provided the number of night and day flushes compared by group. The vasomotor symptom day-to-day intensity change was assessed by therapy assignment. Hot flushes/night sweats were well controlled in both groups, one occurred on average every third day and every fourth night. Mean/day daytime occurrences were 0.363 and 0.187 with CEE and MPA respectively, but were not significantly different (P=0.156). Night sweats also did not differ significantly (P=0.766). Therapies were statistically equivalent (within one event/24 h) in the control of vasomotor symptoms. Day-to-day hot flush intensity decreased with MPA and tended to remain stable with CEE (P<0.001). In conclusion, this analysis demonstrates that MPA and CEE are equivalent and effective in the control of the number of hot flushes/night sweats immediately following premenopausal ovariectomy.

Neonatal intensive care unit characteristics affect the incidence of severe intraventricular hemorrhage.

OBJECTIVES: The incidence of intraventricular hemorrhage (IVH), adjusted for known risk factors, varies across neonatal intensive care units (NICU)s. The effect of NICU characteristics on this variation is unknown. The objective was to assess IVH attributable risks at both patient and NICU levels. STUDY DESIGN: Subjects were <33 weeks' gestation, <4 days old on admission in the Canadian Neonatal Network database (all infants admitted in 1996-97 to 17 NICUs). The variation in severe IVH rates was analyzed using Bayesian hierarchical modeling for patient level and NICU level factors. RESULTS: Of 3772 eligible subjects, the overall crude incidence rates of grade 3-4 IVH was 8.3% (NICU range 2.0-20.5%). Male gender, extreme preterm birth, low Apgar score, vaginal birth, outborn birth, and high admission severity of illness accounted for 30% of the severe IVH rate variation; admission day therapy-related variables (treatment of acidosis and hypotension) accounted for an additional 14%. NICU characteristics, independent of patient level risk factors, accounted for 31% of the variation. NICUs with high patient volume and high neonatologist/staff ratio had lower rates of severe IVH. CONCLUSIONS: The incidence of severe IVH is affected by NICU characteristics, suggesting important new strategies to reduce this important adverse outcome.