Meningococcal infections: reducing the case fatality rate by giving penicillin before admission to hospital.

OBJECTIVE--To determine whether parenteral penicillin given before admission to hospital reduces the case fatality rate in patients with meningococcal disease. DESIGN--Retrospective analysis of 46 consecutive patients admitted to hospital with meningococcal disease from January 1986 to March 1991. SETTING--District general hospital. MAIN OUTCOME MEASURE--Hospital case fatality rate. RESULTS--None of the 13 patients given parenteral penicillin by the referring doctor before admission died, compared with eight deaths (24%) in 33 patients admitted without such treatment. CONCLUSION--Parenteral penicillin given before admission probably contributed to a reduction in the case fatality rate from meningococcal disease, and primary care physicians should be encouraged to give such treatment immediately on suspicion of the diagnosis before transferring the patient to hospital. Public health physicians are well placed to inform and alert general practitioners of the potential benefit of this action.     

TUBERCULOSIS—Recent Trends in Infection,Disease and Deaths in California

Although fewer patients with tuberculosis are reported in a far advanced stage of the disease than ever before, the proportion of persons dying of tuberculosis whose disease had not previously been diagnosed appears to be increasing. The average length of sanatorium treatment, and the intramural case fatality rates have not shown much decrease. Tuberculosis mortality rates fall during economic depressions and rise with business booms. Epidemics of influenza do not always increase tuberculosis death rates. Common claims to the contrary are not sustained by recent California data.     

Tuberculosis; recent trends in infection,disease and deaths in California

Although fewer patients with tuberculosis are reported in a far advanced stage of the disease than ever before, the proportion of persons dying of tuberculosis whose disease had not previously been diagnosed appears to be increasing. The average length of sanatorium treatment, and the intramural case fatality rates have not shown much decrease. Tuberculosis mortality rates fall during economic depressions and rise with business booms. Epidemics of influenza do not always increase tuberculosis death rates. Common claims to the contrary are not sustained by recent California data.     

Estimating the cure proportion of malignant melanoma,an alternative approach to assess long term survival: A population-based study

ObjectivesA large proportion of patients with cutaneous malignant melanoma (CMM) do not experience excess mortality due to their disease. This group of patients is referred to as the cure proportion. Few studies have examined the possibility of cure for CMM. The aim of this study was to estimate the cure proportion of patients with CMM in a Swedish population.MethodsWe undertook a population-based study of 5850 CMM patients in two Swedish health care regions during 1996–2005. We used flexible parametric cure models to estimate cure proportions and median survival times (MSTs) of uncured by stage, sex, age and anatomical site.ResultsDisease stage at diagnosis was the most important factor for the probability of cure, with a cure proportion of approximately 1.0 for stage IA. While the probability of cure decreased with older age, the influence of age was smaller on the MST of uncured. Differences in prognosis between males and females were mainly attributed to differences in cure as opposed to differences in MST of uncured.ConclusionsThis population-based study showed approximately 100% cure among stage IA disease. Almost 50% of patients had stage IA disease and the high cure proportion for this large patient group is reassuring.     

Natural history of tuberculosis: duration and fatality of untreated pulmonary tuberculosis in HIV negative patients: a systematic review

Background

The prognosis, specifically the case fatality and duration, of untreated tuberculosis is important as many patients are not correctly diagnosed and therefore receive inadequate or no treatment. Furthermore, duration and case fatality of tuberculosis are key parameters in interpreting epidemiological data.

Methodology and Principal Findings

To estimate the duration and case fatality of untreated pulmonary tuberculosis in HIV negative patients we reviewed studies from the pre-chemotherapy era. Untreated smear-positive tuberculosis among HIV negative individuals has a 10-year case fatality variously reported between 53% and 86%, with a weighted mean of 70%. Ten-year case fatality of culture-positive smear-negative tuberculosis was nowhere reported directly but can be indirectly estimated to be approximately 20%. The duration of tuberculosis from onset to cure or death is approximately 3 years and appears to be similar for smear-positive and smear-negative tuberculosis.

Conclusions

Current models of untreated tuberculosis that assume a total duration of 2 years until self-cure or death underestimate the duration of disease by about one year, but their case fatality estimates of 70% for smear-positive and 20% for culture-positive smear-negative tuberculosis appear to be satisfactory.     

Bayesian and influence function-based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests

In practice, a disease process might involve three ordinal diagnostic stages: the normal healthy stage, the early stage of the disease, and the stage of full development of the disease. Early detection is critical for some diseases since it often means an optimal time window for therapeutic treatments of the diseases. In this study, we propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to construct confidence/credible intervals for the sensitivity of a test to patients in the early diseased stage given a specificity and a sensitivity of the test to patients in the fully diseased stage. Numerical studies are performed to compare the finite sample performances of the proposed approaches with existing methods. The proposed methods are shown to outperform existing methods in terms of coverage probability. A real dataset from the Alzheimer's Disease Neuroimaging Initiative (ANDI) is used to illustrate the proposed methods.     

Reprogramming of human cancer cells to pluripotency for models of cancer progression

The ability to study live cells as they progress through the stages of cancer provides the opportunity to discover dynamic networks underlying pathology, markers of early stages, and ways to assess therapeutics. Genetically engineered animal models of cancer, where it is possible to study the consequences of temporal‐specific induction of oncogenes or deletion of tumor suppressors, have yielded major insights into cancer progression. Yet differences exist between animal and human cancers, such as in markers of progression and response to therapeutics. Thus, there is a need for human cell models of cancer progression. Most human cell models of cancer are based on tumor cell lines and xenografts of primary tumor cells that resemble the advanced tumor state, from which the cells were derived, and thus do not recapitulate disease progression. Yet a subset of cancer types have been reprogrammed to pluripotency or near‐pluripotency by blastocyst injection, by somatic cell nuclear transfer and by induced pluripotent stem cell (iPS) technology. The reprogrammed cancer cells show that pluripotency can transiently dominate over the cancer phenotype. Diverse studies show that reprogrammed cancer cells can, in some cases, exhibit early‐stage phenotypes reflective of only partial expression of the cancer genome. In one case, reprogrammed human pancreatic cancer cells have been shown to recapitulate stages of cancer progression, from early to late stages, thus providing a model for studying pancreatic cancer development in human cells where previously such could only be discerned from mouse models. We discuss these findings, the challenges in developing such models and their current limitations, and ways that iPS reprogramming may be enhanced to develop human cell models of cancer progression.     

A modeling exercise to show why population models should incorporate distinct life histories of dispersers

Dispersal is an important form of movement influencing population dynamics, species distribution and gene flow between populations. In population models, dispersal is often included in a simplified manner by removing a random proportion of the population. Many ecologists now argue that models should be formulated at the level of individuals instead of the population level. To fully understand the effects of dispersal on natural systems, it is therefore necessary to incorporate individual-level differences in dispersal behavior in population models. Here, we parameterized an integral projection model, which allows for studying how individual life histories determine population-level processes, using bulb mites, Rhizoglyphus robini, to assess to what extent dispersal expression (frequency of individuals in the dispersal stage) and dispersal probability affect the proportion of successful dispersers and natal population growth rate. We find that allowing for life-history differences between resident phenotypes and disperser phenotypes shows that multiple combinations of dispersal probability and dispersal expression can produce the same proportion of leaving individuals. Additionally, a given proportion of successful dispersing individuals result in different natal population growth rates. The results highlight that dispersal life histories, and the frequency with which disperser phenotypes occur in the natal population, significantly affect population-level processes. Thus, biological realism of dispersal population models can be increased by incorporating the typically observed life-history differences between resident phenotypes and disperser phenotypes, and we here present a methodology to do so.     

Protective efficacy of neutralizing monoclonal antibodies in a nonhuman primate model of Ebola hemorrhagic fever

Ebola virus (EBOV) is the causative agent of severe hemorrhagic fever in primates, with human case fatality rates up to 90%. Today, there is neither a licensed vaccine nor a treatment available for Ebola hemorrhagic fever (EHF). Single monoclonal antibodies (MAbs) specific for Zaire ebolavirus (ZEBOV) have been successfully used in passive immunization experiments in rodent models, but have failed to protect nonhuman primates from lethal disease. In this study, we used two clones of human-mouse chimeric MAbs (ch133 and ch226) with strong neutralizing activity against ZEBOV and evaluated their protective potential in a rhesus macaque model of EHF. Reduced viral loads and partial protection were observed in animals given MAbs ch133 and ch226 combined intravenously at 24 hours before and 24 and 72 hours after challenge. MAbs circulated in the blood of a surviving animal until virus-induced IgG responses were detected. In contrast, serum MAb concentrations decreased to undetectable levels at terminal stages of disease in animals that succumbed to infection, indicating substantial consumption of these antibodies due to virus replication. Accordingly, the rapid decrease of serum MAbs was clearly associated with increased viremia in non-survivors. Our results indicate that EBOV neutralizing antibodies, particularly in combination with other therapeutic strategies, might be beneficial in reducing viral loads and prolonging disease progression during EHF.     

Utility and efficiency of linked marker genes for genetic counseling. II. Identification of linkage phase by offspring phenotypes

For a linked marker locus to be useful for genetic counseling, the counselee must be heterozygous for both disease and marker loci and his or her linkage phase must be known. It is shown that when the phenotypes of the counselee's previous children for the disease and marker loci are known, the linkage phase can often be inferred with a high probability, and thus it is possible to conduct genetic counseling. To evaluate the utility of linked marker genes for genetic counseling, the accuracy of prediction of the risk for a prospective child with a given marker gene to develop the genetic disease and the proportion of families in which a particular marker locus can be used for genetic counseling are studied for X-linked recessive, autosomal dominant, and autosomal recessive diseases. In the case of X-linked genetic diseases, information from children is very useful for determining the linkage phase of the counselee and predicting the genetic disease. In the case of autosomal dominant diseases, not all children are informative, but if the number of children is large, the phenotypes of children are often more informative than the information from grandparents. In the case of autosomal recessive diseases, information from grandparents is usually useless, since they show a normal phenotype for the disease locus. If we use information on the phenotypes of children, however, the linkage phase of the counselee and the risk of a prospective child can be inferred with a high probability. The proportion of informative families depends on the dominance relationship and frequencies of marker alleles, and the number of children. In general, codominant markers are more useful than are dominant markers, and a locus with high heterozygosity is more useful than is a locus with low heterozygosity.     

Epidemiological Models with Non-Exponentially Distributed Disease Stages and Applications to Disease Control

SEIR epidemiological models with the inclusion of quarantine and isolation are used to study the control and intervention of infectious diseases. A simple ordinary differential equation (ODE) model that assumes exponential distribution for the latent and infectious stages is shown to be inadequate for assessing disease control strategies. By assuming arbitrarily distributed disease stages, a general integral equation model is developed, of which the simple ODE model is a special case. Analysis of the general model shows that the qualitative disease dynamics are determined by the reproductive number , which is a function of control measures. The integral equation model is shown to reduce to an ODE model when the disease stages are assumed to have a gamma distribution, which is more realistic than the exponential distribution. Outcomes of these models are compared regarding the effectiveness of various intervention policies. Numerical simulations suggest that models that assume exponential and non-exponential stage distribution assumptions can produce inconsistent predictions.     

Combining assays for estimating prevalence of human herpesvirus 8 infection using multivariate mixture models

For many diseases, it is difficult or impossible to establish a definitive diagnosis because a perfect "gold standard" may not exist or may be too costly to obtain. In this paper, we propose a method to use continuous test results to estimate prevalence of disease in a given population and to estimate the effects of factors that may influence prevalence. Motivated by a study of human herpesvirus 8 among children with sickle-cell anemia in Uganda, where 2 enzyme immunoassays were used to assess infection status, we fit 2-component multivariate mixture models. We model the component densities using parametric densities that include data transformation as well as flexible transformed models. In addition, we model the mixing proportion, the probability of a latent variable corresponding to the true unknown infection status, via a logistic regression to incorporate covariates. This model includes mixtures of multivariate normal densities as a special case and is able to accommodate unusual shapes and skewness in the data. We assess model performance in simulations and present results from applying various parameterizations of the model to the Ugandan study.     

An early- and late-stage convolution model for disease natural history

The standard convolution model of disease natural history posits an asymptomatic (preclinical) and a symptomatic (clinical) state. An augmented model includes, in both the preclinical and clinical states, an early and late stage of disease. In the case of cancer, the early stage would generally correspond to the organ-confined stages before there is evidence of cancer spread. We compute the number of screen-detected (preclinical) and clinical cases in the early and late stages expected under a given screening program and show how the model can be fit to data from a screening trial using maximum likelihood. We also develop expressions for sojourn time, lead time, and overdiagnosis in the context of the model, where each of the above concepts incorporates disease stage. As an example, we fit the model to data from the Mayo Lung Cancer Screening trial.     

Red-Shouldered Hawk Occupancy Surveys in Central Minnesota,USA

ABSTRACT Forest-dwelling raptors are often difficult to detect because many species occur at low density or are secretive. Broadcasting conspecific vocalizations can increase the probability of detecting forest-dwelling raptors and has been shown to be an effective method for locating raptors and assessing their relative abundance. Recent advances in statistical techniques based on presence—absence data use probabilistic arguments to derive probability of detection when it is < 1 and to provide a model and likelihood-based method for estimating proportion of sites occupied. We used these maximum-likelihood models with data from red-shouldered hawk (Buteo lineatus) call-broadcast surveys conducted in central Minnesota, USA, in 1994–1995 and 2004–2005. Our objectives were to obtain estimates of occupancy and detection probability 1) over multiple sampling seasons (yr), 2) incorporating within-season time-specific detection probabilities, 3) with call type and breeding stage included as covariates in models of probability of detection, and 4) with different sampling strategies. We visited individual survey locations 2–9 times per year, and estimates of both probability of detection (range = 0.28-0.54) and site occupancy (range = 0.81-0.97) varied among years. Detection probability was affected by inclusion of a within-season time-specific covariate, call type, and breeding stage. In 2004 and 2005 we used survey results to assess the effect that number of sample locations, double sampling, and discontinued sampling had on parameter estimates. We found that estimates of probability of detection and proportion of sites occupied were similar across different sampling strategies, and we suggest ways to reduce sampling effort in a monitoring program.     

Germinal mosaicism and risk calculation in X-linked diseases.

Germinal mosaicism is a major problem in risk estimation for an X-linked disease. A mutation can happen anytime in germ cell development, and the proportion of germ cells bearing the mutated gene is twice the probability of recurrence of the mutation. This proportion could be either very low in late mutations or very high in germinal and somatic mosaicism. When this heterogeneity is taken into consideration, the distribution of the recurrence risk is conveniently represented as a set of discrete classes that may be derived either from models of gametogenesis or from empirical data. A computer program taking into account germinal mosaicism has been devised to calculate the probability of a possible carrier belonging to any of these classes, in order to settle the origin of the mutation of a given family. Germinal mosaicism increases the probability of inheriting the mutation, but this effect is always lowered by the possibility of heterogeneity. When the mother of a possible carrier is not herself a carrier, the risk of her daughter being a carrier is approximately halved, even under the assumption of a high recurrence risk from mosaicism.     

Sex differences in case fatality before and after admission to hospital after acute cardiac events: analysis of community based coronary heart disease register.

OBJECTIVE: To determine whether the reported higher case fatality in hospital after an acute cardiac event in women can be explained by sex differences in mortality before admission and in baseline risk factors. DESIGN: Analyses of data from a community based coronary heart disease register. SETTING: Auckland region, New Zealand. SUBJECTS: 5106 patients aged 25-64 years with an acute cardiac event leading to coronary death or definite myocardial infarction within 28 days of onset, occurring between 1986 and 1992. MAIN OUTCOME MEASURES: Case fatality before admission, 28 day case fatality for patients in hospital, and total case fatality after an acute cardiac event. RESULTS: Despite a more unfavourable risk profile women tended to have lower case fatality before admission than men (crude odds ratio 0.88; 95% confidence interval 0.77 to 1.02). Adjustment for age, living arrangements, smoking, medical history, and treatment increased the effect of sex (0.72; 0.60 to 0.86). After admission to hospital, women had a higher case fatality than men (1.76; 1.43 to 2.17), but after adjustment for confounders this was reduced to 1.18 (0.89 to 1.58). Total case fatality 28 days after an acute cardiac event showed no significant difference between men and women (0.85; 0.70 to 1.02) CONCLUSIONS: The higher case fatality after an acute cardiac event in women admitted to hospital is largely explained by differences in living status, history, and medical treatment and is balanced by a lower case fatality before admission.     

Early onset of neurological features differentiates two outbreaks of Lassa fever in Ebonyi state,Nigeria during 2017–2018

Lassa fever (LF) is an acute viral haemorrhagic illness with various non-specific clinical manifestations. Neurological symptoms are rare at the early stage of the disease, but may be seen in late stages, in severely ill patients.The aim of this study was to describe the epidemiological evolution, socio-demographic profiles, clinical characteristics, and outcomes of patients seen during two Lassa fever outbreaks in Ebonyi State, between December 2017 and December 2018.Routinely collected clinical data from all patients admitted to the Virology Centre of the hospital during the period were analysed retrospectively. Out of a total of 83 cases, 70(84.3%) were RT-PCR confirmed while 13 (15.7%) were probable cases. Sixty-nine (83.1%) patients were seen in outbreak 1 of whom 53.6% were urban residents, while 19%, 15%, and 10% were farmers, students and health workers respectively. There were 14 (16.8%) patients, seen in second outbreak with 92.9% rural residents. There were differences in clinical symptoms, signs and laboratory findings between the two outbreaks. The case fatality rates were 29.9% in outbreak 1 and 85.7% for outbreak 2. Neurological features and abnormal laboratory test results were associated with higher mortality rate, seen in outbreak 2. This study revealed significant differences between the two outbreaks. Of particular concern was the higher case fatality during the outbreak 2 which may be from a more virulent strain of the Lassa virus. This has important public health implications and further molecular studies are needed to better define its characteristics.     

Adaptive designs with arbitrary dependence structure

Adaptive designs were originally developed for independent and uniformly distributed p‐values. There are trial settings where independence is not satisfied or where it may not be possible to check whether it is satisfied. In these cases, the test statistics and p‐values of each stage may be dependent. Since the probability of a type I error for a fixed adaptive design depends on the true dependence structure between the p‐values of the stages, control of the type I error rate might be endangered if the dependence structure is not taken into account adequately. In this paper, we address the problem of controlling the type I error rate in two‐stage adaptive designs if any dependence structure between the test statistics of the stages is admitted (worst case scenario). For this purpose, we pursue a copula approach to adaptive designs. For two‐stage adaptive designs without futility stop, we derive the probability of a type I error in the worst case, that is for the most adverse dependence structure between the p‐values of the stages. Explicit analytical considerations are performed for the class of inverse normal designs. A comparison with the significance level for independent and uniformly distributed p‐values is performed. For inverse normal designs without futility stop and equally weighted stages, it turns out that correcting for the worst case is too conservative as compared to a simple Bonferroni design.     

Spatially-explicit wildlife surveillance to prove freedom from diseases or pests

Surveillance undertaken to prove freedom from a disease has traditionally focussed on sampling a specified proportion of the population to determine the presence of the infective agent. Based on a sample of individuals testing negative for disease, standard probability theory using the binomial or hypergeometric distribution can be used to make inference about the probability of disease being absent. However, if the disease agent involves a wildlife host, then determining what proportion of the population was sampled becomes problematic as it requires an estimate of population abundance, which may be difficult and expensive to obtain. Surveillance during the eradication of pests such as feral ungulates often make use of the Judas technique, whereby radio-collared individuals are released and followed in the hope that the sociality of the individuals will betray the locations of conspecifics, which can then be dispatched. Data collected during such surveillance is explicitly spatial, containing information about the ‘search area’ of the Judas animal. However, there has been no attempt to use such data to make inference about the probability of the area being free of the pest, given Judas animals fail to detect any conspecifics. We present a framework for making specific use of the spatial nature of such wildlife surveillance data to make inference about the probability of freedom from the disease or pest. Underlying the sampling framework is a model of the detection process by sampled individuals. Estimates of individual detection probability are spatially smoothed using the extent of individual movements to produce a spatially- explicit detection surface. Bayes theorem is then used to combine this 2-dimensional surface with prior information on the probability of pest or disease presence, prior to sampling, to estimate the probability that the area is free from the disease or pest, given surveillance fails to detect evidence of their presence. We illustrate the method with examples of the detection of bovine Tb in wildlife in New Zealand and the detection of pigs (Sus scrofa) using the Judas technique during the feral pig eradication programme on Santa Cruz Island, California.     

A robust method using propensity score stratification for correcting verification bias for binary tests

Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends on the true (missing) disease status only through the test result and observed covariate information. When some of the covariates are continuous, or the number of covariates is relatively large, the existing methods require parametric models for the probability of disease or the probability of verification (given the test result and covariates), and hence are subject to model misspecification. We propose a new method for correcting verification bias based on the propensity score, defined as the predicted probability of verification given the test result and observed covariates. This is estimated separately for those with positive and negative test results. The new method classifies the verified sample into several subsamples that have homogeneous propensity scores and allows correction for verification bias. Simulation studies demonstrate that the new estimators are more robust to model misspecification than existing methods, but still perform well when the models for the probability of disease and probability of verification are correctly specified.