Estimation of the basic reproductive number and mean serial interval of a novel pathogen in a small, well-observed discrete population

BACKGROUND:Accurately assessing the transmissibility and serial interval of a novel human pathogen is public health priority so that the timing and required strength of interventions may be determined. Recent theoretical work has focused on making best use of data from the initial exponential phase of growth of incidence in large populations. METHODS:We measured generational transmissibility by the basic reproductive number R0 and the serial interval by its mean Tg. First, we constructed a simulation algorithm for case data arising from a small population of known size with R0 and Tg also known. We then developed an inferential model for the likelihood of these case data as a function of R0 and Tg. The model was designed to capture a) any signal of the serial interval distribution in the initial stochastic phase b) the growth rate of the exponential phase and c) the unique combination of R0 and Tg that generates a specific shape of peak incidence when the susceptible portion of a small population is depleted. FINDINGS:Extensive repeat simulation and parameter estimation revealed no bias in univariate estimates of either R0 and Tg. We were also able to simultaneously estimate both R0 and Tg. However, accurate final estimates could be obtained only much later in the outbreak. In particular, estimates of Tg were considerably less accurate in the bivariate case until the peak of incidence had passed. CONCLUSIONS:The basic reproductive number and mean serial interval can be estimated simultaneously in real time during an outbreak of an emerging pathogen. Repeated application of these methods to small scale outbreaks at the start of an epidemic would permit accurate estimates of key parameters

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

The HI Content of the Universe over the Past 10 Gyrs

We use the Hubble Space Telescope (HST) archive of ultraviolet (UV) quasar spectroscopy to conduct the first blind survey for damped Ly-alpha absorbers (DLAs) at low redshift (z < 1.6). Our statistical sample includes 463 quasars with spectral coverage spanning a total redshift path, dz = 123.3 or an absorption path, dX = 229.7. Within this survey path, we identify 4 DLAs, defined as absorbers with HI column density N(HI) >= 10^20.3cm-2, which implies an incidence per absorption length, l(X)= 0.017(+0.014-0.008) at a median survey path redshift of z=0.623. While our estimate of l(X) is lower than earlier estimates at z ~ 0 from HI 21cm emission studies, the results are consistent within the measurement uncertainties. Our dataset is too small to properly sample the N(HI) frequency distribution function f(N(HI),X), but the observed distribution agrees with previous estimates at z > 2. Adopting the z > 2 shape of f(N(HI),X), we infer an HI mass density at z ~ 0.6 of rho_HI = 0.25(+0.20-0.12) x 10^8 Msol Mpc-3. This is significantly lower than previous estimates from targeted DLA surveys with the HST, but consistent with results from low-z HI 21cm observations, and suggests that the neutral gas density of the universe has been decreasing over the past 10 Gyrs.Comment: 28 pages, 6 figure

Extending backcalculation to analyse BSE data.

We review the origins of backcalculation (or back projection) methods developed for the analysis of AIDS (acquired immunodeficiency syndrome) incidence data. These techniques have been used extensively for >15 years to deconvolute clinical case incidence, given knowledge of the incubation period distribution, to obtain estimates of past HIV (human immunodeficiency virus) infection incidence and short-term predictions of future AIDS incidence. Adaptations required for the analysis of bovine spongiform encephalopathy (BSE) incidence included: stratification of BSE incidence by age as well as birth cohort; allowance for incomplete survival between infection and the onset of clinical signs of disease; and decomposition of the age- and time-related infection incidence into a time-dependent feed risk component and an age-dependent exposure/susceptibility function. The most recent methodological developments focus on the incorporation of data from clinically unaffected cattle screened using recently developed tests for preclinical BSE infection. Backcalculation-based predictions of future BSE incidence obtained since 1996 are examined. Finally, future directions of epidemiological analysis of BSE epidemics are discussed taking into account ongoing developments in the science of BSE and possible changes in BSE-related policies

The direct boundary element method: 2D site effects assessment on laterally varying layered media (methodology)

The Direct Boundary Element Method (DBEM) is presented to solve the elastodynamic field equations in 2D, and a complete comprehensive implementation is given. The DBEM is a useful approach to obtain reliable numerical estimates of site effects on seismic ground motion due to irregular geological configurations, both of layering and topography. The method is based on the discretization of the classical Somigliana's elastodynamic representation equation which stems from the reciprocity theorem. This equation is given in terms of the Green's function which is the full-space harmonic steady-state fundamental solution. The formulation permits the treatment of viscoelastic media, therefore site models with intrinsic attenuation can be examined. By means of this approach, the calculation of 2D scattering of seismic waves, due to the incidence of P and SV waves on irregular topographical profiles is performed. Sites such as, canyons, mountains and valleys in irregular multilayered media are computed to test the technique. The obtained transfer functions show excellent agreement with already published results

The frequency and validity of self-reported diagnosis of Parkinson's Disease in the UK elderly: MRC CFAS cohort

Background: Estimates of the incidence and prevalence of chronic diseases can be made using established cohort studies but these estimates may have lower reliability if based purely on self-reported diagnosis.Methods: The MRC Cognitive Function & Ageing Study ( MRC CFAS) has collected longitudinal data from a population-based random sample of 13004 individuals over the age of 65 years from 5 centres within the UK. Participants were asked at baseline and after a two-year follow-up whether they had received a diagnosis of Parkinson's disease. Our aim was to make estimates of the incidence and prevalence of PD using self-reporting, and then investigate the validity of self-reported diagnosis using other data sources where available, namely death certification and neuropathological examination.Results: The self-reported prevalence of Parkinson's disease ( PD) amongst these individuals increases with age from 0.7% (95% CI 0.5 - 0.9) for 65 - 75, 1.4% ( 95% CI 1.0 - 1.7) for 75 - 85, and 1.6% ( 95% CI 1.0 - 2.3) for 85+ age groups respectively. The overall incidence of self reported PD in this cohort was 200/100,000 per year ( 95% CI 144 - 278). Only 40% of the deceased individuals reporting prevalent PD and 35% of those reporting incident PD had diagnoses of PD recorded on their death certificates. Neuropathological examination of individuals reporting PD also showed typical PD changes in only 40%, with the remainder showing basal ganglia pathologies causing parkinsonism rather than true PD pathology.Conclusion: Self-reporting of PD status may be used as a screening tool to identify patients for epidemiological study, but inevitably identifies a heterogeneous group of movement disorders patients. Within this group, age, male sex, a family history of PD and reduced cigarette smoking appear to act as independent risk factors for self-reported PD

Identifiability and estimation of the sign of a covariate effect in the competing risks model.

It is well known that the competing risks model is identified if the dependence structure between risks (the copula function) is known or assumed. Special cases include independence of risks or independent censoring. If the copula function is not specified, parameters of interest are only set identified. As these sets are often wide in applications, it is difficult to obtain informative results. In this paper we strike a balance between imposing too much and too little structure. By establishing a general link between observable changes in subdistributions (cumulative incidence curves) and the sign of changes in marginal distributions (the causal treatment effect) we are able to show the identifiability of the latter if the copula function is independent of the varying covariate. This has two important implications: First, it is possible to obtain informative results even if the copula function is mainly unspecified or unknown. Second, the sign of the covariate effect tends to be invariant with respect to the chosen dependence structure. Our method is computationally very simple and our simulations suggest that it identifies and consistently estimates the sign of the treatment effect for large sets of duration times. An application to unemployment duration data illustrates the usefulness of our method for empirical research.dependent censoring, nonparametric estimation, bootstrap

Duration and Risk of Unemployment in Argentina

After a decade of structural reforms, unemployment rates have tripled in Argentina. This paper is concerned with the measurement of unemployment risk and its distribution. We show the importance of considering re-incidence in the measurement of unemployment risk and develop a methodolgy to do that. Our estimates for Argentina show that, though the typical unemployment spell is short, once re-incidence is taken into account, unemployment risk is high, has risen substantially in the last decade and is shared very unequally in the labor force. This counters the established view that unemployment is a small risk, short-duration phenomenon, which arises when re-incidence is not considered.http://deepblue.lib.umich.edu/bitstream/2027.42/39861/3/wp476.pd

Comparisons Between the Kaplan-Meier Complement and the Cumulative Incidence for Survival Prediction in the Presence of Competing Events

Estimating cumulative event probabilities in time-to-event data can be complicated by competing events. Competing events occur when individuals can experience events other than the primary event of interest. These “other events” are often treated as censored observations. This thesis compares point estimates and relative differences between two cumulative event probability estimators, the Kaplan-Meier complement (KMC) and the cumulative incidence (CI), in the presence of competing events. The KMC does not allow for the possibility of experiencing competing events, whereas the CI does. Consequently, the KMC overestimates the CI in the presence of competing events. In this thesis, data were simulated with different combinations of primary event hazards, competing event hazards, random censoring hazards, and sample sizes. Cumulative event probabilities using the KMC and CI methods were calculated over a time period of 10 years. Several conclusions were drawn. High primary event hazards resulted in high KMC’s and CI’s and slightly narrowed the variability of the relative differences between the two estimates. High competing event hazards did not affect KMC’s but resulted in low CI’s, causing high relative differences. High random censoring hazards did not affect KMC’s, CI’s, or relative differences. Large sample sizes did not affect the median relative differences but did narrow the variability of the relative differences. The public health relevance of this thesis is to help medical clinicians and researchers understand the advantages and disadvantages of different approaches of calculating cumulative event probabilities in situations where competing events occur. This is particularly important in the area of personalized medicine in diseases like cancer where clinicians attempt to predict their patients' mortality or recurrence probabilities over time given certain clinical, pathologic, or demographic characteristics