Spatial modelling of individual-level parasite counts using the negative binomial distribution

We present a spatial model for the mean and correlation of highly dispersed count data, and apply it to individual-level counts of the nematode Wuchereria bancrofti, a parasite of humans which causes the disease lymphatic filariasis. Our model uses the negative binomial distribution, whose shape parameter is a convenient index of over-dispersion. Spatial association is quantified in terms of a characteristic length, which has an intuitive interpretation as the distance over which correlation decreases by half. Demographic surveillance and mapping enable us to include individual-level covariates such as age and sex. We discuss the distinctive features of our model and interpret the results in terms of the epidemiology of lymphatic filariasis and possible implications for control programmes.     

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656–665) and Hou et al. (2020, Biostatistics, 21, 417–431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article.     

Morris Water Maze Test: Optimization for Mouse Strain and Testing Environment

The Morris water maze (MWM) is a commonly used task to assess hippocampal-dependent spatial learning and memory in transgenic mouse models of disease, including neurocognitive disorders such as Alzheimer’s disease. However, the background strain of the mouse model used can have a substantial effect on the observed behavioral phenotype, with some strains exhibiting superior learning ability relative to others. To ensure differences between transgene negative and transgene positive mice can be detected, identification of a training procedure sensitive to the background strain is essential. Failure to tailor the MWM protocol to the background strain of the mouse model may lead to under- or over- training, thereby masking group differences in probe trials. Here, a MWM protocol tailored for use with the F1 FVB/N x 129S6 background is described. This is a frequently used background strain to study the age-dependent effects of mutant P301L tau (rTg(TauP301L)4510 mice) on the memory deficits associated with Alzheimer’s disease. Also described is a strategy to re-optimize, as dictated by the particular testing environment utilized.     

A hierarchical aggregate data model with spatially correlated disease rates

The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model.     

A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.     

Inter-observer reliability of animal-based welfare indicators included in the Animal Welfare Indicators welfare assessment protocol for dairy goats

This study was conducted within the context of the Animal Welfare Indicators (AWIN) project and the underlying scientific motivation for the development of the study was the scarcity of data regarding inter-observer reliability (IOR) of welfare indicators, particularly given the importance of reliability as a further step for developing on-farm welfare assessment protocols. The objective of this study is therefore to evaluate IOR of animal-based indicators (at group and individual-level) of the AWIN welfare assessment protocol (prototype) for dairy goats. In the design of the study, two pairs of observers, one in Portugal and another in Italy, visited 10 farms each and applied the AWIN prototype protocol. Farms in both countries were visited between January and March 2014, and all the observers received the same training before the farm visits were initiated. Data collected during farm visits, and analysed in this study, include group-level and individual-level observations. The results of our study allow us to conclude that most of the group-level indicators presented the highest IOR level (‘substantial’, 0.85 to 0.99) in both field studies, pointing to a usable set of animal-based welfare indicators that were therefore included in the first level of the final AWIN welfare assessment protocol for dairy goats. Inter-observer reliability of individual-level indicators was lower, but the majority of them still reached ‘fair to good’ (0.41 to 0.75) and ‘excellent’ (0.76 to 1) levels. In the paper we explore reasons for the differences found in IOR between the group and individual-level indicators, including how the number of individual-level indicators to be assessed on each animal and the restraining method may have affected the results. Furthermore, we discuss the differences found in the IOR of individual-level indicators in both countries: the Portuguese pair of observers reached a higher level of IOR, when compared with the Italian observers. We argue how the reasons behind these differences may stem from the restraining method applied, or the different background and experience of the observers. Finally, the discussion of the results emphasizes the importance of considering that reliability is not an absolute attribute of an indicator, but derives from an interaction between the indicators, the observers and the situation in which the assessment is taking place. This highlights the importance of further considering the indicators’ reliability while developing welfare assessment protocols.     

The Limits of Test-Based Scrapie Eradication Programs in Goats

Small ruminant post-mortem testing programs were initially designed for monitoring the prevalence of prion disease. They are now considered as a potential alternative to genetic selection for eradicating/controlling classical scrapie at population level. If such policy should be implemented, its success would be crucially dependent on the efficiency of the surveillance system used to identify infected flocks. In this study, we first determined the performance of post-mortem classical scrapie detection in eight naturally affected goat herds (total n = 1961 animals) according to the age at culling. These results provided us with necessary parameters to estimate, through a Monte Carlo simulation model, the performance of scrapie detection in a commercial population. According to this model, whatever the number of tests performed, post mortem surveillance will have limited success in identifying infected herds. These data support the contention that scrapie eradication programs relying solely on post mortem testing in goats will probably fail. Considering the epidemiological and pathological similarities of scrapie in sheep and goats, the efficiency of scrapie surveillance in both species is likely to be similar.     

What Can Cross-Cultural Correlations Teach Us about Human Nature?

Many recent evolutionary psychology and human behavioral ecology studies have tested hypotheses by examining correlations between variables measured at a group level (e.g., state, country, continent). In such analyses, variables collected for each aggregation are often taken to be representative of the individuals present within them, and relationships between such variables are presumed to reflect individual-level processes. There are multiple reasons to exercise caution when doing so, including: (1) the ecological fallacy, whereby relationships observed at the aggregate level do not accurately represent individual-level processes; (2) non-independence of data points, which violates assumptions of the inferential techniques used in null hypothesis testing; and (3) cross-cultural non-equivalence of measurement (differences in construct validity between groups). We provide examples of how each of these gives rise to problems in the context of testing evolutionary hypotheses about human behavior, and we offer some suggestions for future research.     

The effects of in-utero exposure to influenza on mental health and mortality risk throughout the life-course

Studies examining the later-life health consequences of in-utero exposure to influenza have typically estimated effects on physical health conditions, with little evidence of effects on mental health outcomes or mortality. Previous studies have also relied primarily on reduced-form estimates of the effects of exposure to influenza pandemics, meaning they are unlikely to recover effects of influenza exposure at an individual-level. This paper uses inverse probability of treatment weighting and “doubly-robust” methods alongside rare mother-reported data on in-utero influenza exposure to estimate the individual-level effect of in-utero influenza exposure on mental health and mortality risk throughout childhood and adulthood. We find that in-utero exposure to influenza is associated with small reductions in mental health in mid-childhood, driven by increases in internalising symptoms, and increases in depressive symptoms in mid-life for males. There is also evidence that in-utero influenza exposure is associated with substantial increases in mortality, although these effects are primarily driven by a 75% increase in the probability of being stillborn, with limited evidence of additional survival disadvantages at later ages. The potential for mortality selection implies that estimated effects on mental health outcomes are likely to represent a lower bound.     

Impact of High Deductible Health Plans on Diabetes Care Quality and Outcomes: Systematic Review

ObjectiveTo provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes.MethodsSystematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications).ResultsOf the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control.ConclusionAlthough HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients’ health.     

Insights on bias and information in group-level studies

Ecological and aggregate data studies are examples of group-level studies. Even though the link between the predictors and outcomes is not preserved in these studies, inference about individual-level exposure effects is often a goal. The disconnection between the level of inference and the level of analysis expands the array of potential biases that can invalidate the inference from group-level studies. While several sources of bias, specifically due to measurement error and confounding, may be more complex in group-level studies, two sources of bias, cross-level and model specification bias, are a direct consequence of the disconnection. With the goal of aligning inference from individual versus group-level studies, I discuss the interplay between exposure and study design. I specify the additional assumptions necessary for valid inference, specifically that the between- and within-group exposure effects are equal. Then cross-level inference is possible. However, all the information in the group-level analysis comes from between-group comparisons. Models where the group-level analysis provides even a small percentage of information about the within-group exposure effect are most susceptible to model specification bias. Model specification bias can be even more serious when the group-level model isn't derived from an individual-level model.     

Surveillance protocols for management of invasive plants: modelling Chilean needle grass (Nassella neesiana) in Australia

Aim To develop a surveillance support model that enables prediction of areas susceptible to invasion, comparative analysis of surveillance methods and intensity and assessment of eradication feasibility. To apply the model to identify surveillance protocols for generalized invasion scenarios and for evaluating surveillance and control for a context‐specific plant invasion. Location Australia. Methods We integrate a spatially explicit simulation model, including plant demography and dispersal vectors, within a Geographical Information System. We use the model to identify effective surveillance protocols using simulations of generalized plant life‐forms spreading via different dispersal mechanisms in real landscapes. We then parameterize the surveillance support model for Chilean needle grass [CNG; Nassella neesiana (Trin. & Rupr.) Barkworth], a highly invasive tussock grass, which is an eradication target in south‐eastern Queensland, Australia. Results General surveillance protocols that can guide rapid response surveillance were identified; suitable habitat that is susceptible to invasion through particular dispersal syndromes should be targeted for surveillance using an adaptive seek‐and‐destroy method. The search radius of the adaptive method should be based on maximum expected dispersal distances. Protocols were used to define a surveillance strategy for CNG, but simulations indicated that despite effective and targeted surveillance, eradication is implausible at current intensities. Main conclusions Several important surveillance protocols emerged and simulations indicated that effectiveness can be increased if they are followed in rapid response surveillance. If sufficient data are available, the surveillance support model should be parameterized to target areas susceptible to invasion and determine whether surveillance is effective and eradication is feasible. We discovered that for CNG, regardless of a carefully designed surveillance strategy, eradication is implausible at current intensities of surveillance and control and these efforts should be doubled if they are to be successful. This is crucial information in the face of environmentally and economically damaging invasive species and large, expensive and potentially ineffective control programmes.     

Estimating the true prevalence of Mycobacterium bovis in hunter-harvested white-tailed deer in Michigan

Apparent prevalence, although useful as a consistent index, may underestimate the true prevalence of disease. In Michigan, the ability to estimate the true prevalence of bovine tuberculosis (TB; caused by Mycobacterium bovis) in free-ranging white-tailed deer (Odocoileus virginianus) will become increasingly important to accurately assess progress towards eradication. Our objectives were threefold: to estimate the true prevalence of M. bovis in free-ranging deer in Michigan, to evaluate the effectiveness of existing TB surveillance methods, and to indirectly assess whether TB epidemiologic data from captive cervid herds can be meaningfully extrapolated to free-ranging populations. The study population consisted of all free-ranging deer submitted for TB testing in 2001 from six townships in northeastern Lower Michigan. Tissue samples of tonsil and cranial lymph nodes were collected bilaterally from all deer eligible for the study that did not have gross lesions suggestive of TB (n = 701). Samples were subjected to histopathologic, acid-fast (AF) staining, mycobacterial culture, and polymerase chain reaction (PCR) testing. Seven deer cultured positive for M. bovis that would not have been detected by current surveillance, yielding apparent and true prevalence estimates (95% confidence limits) of 2.7% (1.6, 3.8) and 3.6% (2.3, 4.9), respectively. The sensitivity, specificity, and positive and negative predictive values of the current surveillance protocol were 75, 100, 100, and 99%, respectively. Histologic lesions were present only in tonsils, and ranged from simple necrosis to caseation, suppuration, and granuloma formation. Acid-fast staining and PCR detected M. bovis in only one of the seven culture-positive deer. Our study provides the first estimate of the true prevalence of M. bovis in Michigan's free-ranging deer population and suggests modest underestimation of that prevalence by current surveillance. This study also suggests that caution is warranted when extrapolating epidemiologic data on TB in captive cervids to free-ranging populations and confirms the pivotal role of the tonsil in early infections.     

Mortality modeling of early detection programs

Summary .   Consider a group of subjects who are offered an opportunity to receive a sequence of periodic special examinations for the purpose of diagnosing a chronic disease earlier relative to usual care. The mortality for the early detection group is to be compared with a group receiving usual care. Benefit is reflected in a potential reduction in mortality. This article develops a general probability model that can be used to predict cumulative mortality for each of these groups. The elements of the model assume (i) a four-state progressive disease model in which a subject may be in a disease-free state (or a disease state that cannot be detected), preclinical disease state (capable of being diagnosed by a special exam), clinical state (diagnosis by usual care), and a death state; (ii) age-dependent transitions into the states; (iii) age-dependent examination sensitivity; (iv) age-dependent sojourn time in each state; and (v) the distribution of disease stages on diagnosis conditional on modality of detection. The model may be used to (i) compare mortality rates for different screening schedules; (ii) explore potential benefit of subpopulations; and (iii) compare relative reductions in disease-specific mortality due to advances and dissemination of both treatment and early detection screening programs.     

Spatial event cluster detection using a compound Poisson distribution

Geographic disease surveillance methods identify regions that have higher disease rates than expected. These approaches are generally applied to incident or prevalent cases of disease. In some contexts, disease-related events rather than individuals are the appropriate units of analysis for geographic surveillance. We propose a compound Poisson approach that detects event clusters by testing individual areas that may be combined with their nearest neighbors. The method is applicable to situations where the population sizes are diverse and the population distribution by important strata may differ by area. For example, a geographical region might have sparse population in the northern areas, and other areas which are predominantly retirement communities. The approach requires a coarse geographical relationship and administrative data for the numbers of population, cases, and events in each area. Pediatric self-inflicted injuries requiring presentation to Alberta emergency departments provide an illustration.     

Impact of prescribed cleaning and disinfectant use on microbial contamination in the home

Aims: To identify and quantify the presence of Escherichia coli, Staphylococcus aureus, Salmonella, hepatitis A and norovirus in households and to assess the effect of chlorine and quaternary ammonium–based disinfectants following a prescribed use. Methods and Results: Eleven sites distributed in kitchen, bathroom, pet and children′s areas of two groups of 30 homes each: (i) a nonprescribed disinfectant user group and (ii) a disinfectant protocol user group. During the 6‐week study, samples were collected once a week except for week one when sample collection occurred immediately before and after disinfectant application to evaluate the disinfectant protocol. The concentration and occurrence of bacteria were less in the households with prescribed use of disinfectants. The greatest reductions were for E. coli (99%) and Staph. aureus (99·9999%), respectively. Only two samples were positive for HAV, while norovirus was absent. Disinfection protocols resulted in a significant (P < 0·05) microbial reduction in all areas of the homes tested compared to homes not using a prescribed protocol. Conclusions: The study suggests that disinfectant product application under specific protocol is necessary to achieve greater microbial reductions. Significance and Impact of the Study: Prescribed protocols constitute an important tool to reduce the occurrence of potential disease‐causing micro‐organisms in households.     

Fair regression for health care spending

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrates that these new fair regression methods may lead to massive improvements in group fairness (eg, 98%) with only small reductions in overall fit (eg, 4%).     

Modeling seasonality in space‐time infectious disease surveillance data

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one‐step‐ahead predictions if all three components allow for seasonal variation.     

A tree-based scan statistic for database disease surveillance

Many databases exist with which it is possible to study the relationship between health events and various potential risk factors. Among these databases, some have variables that naturally form a hierarchical tree structure, such as pharmaceutical drugs and occupations. It is of great interest to use such databases for surveillance purposes in order to detect unsuspected relationships to disease risk. We propose a tree-based scan statistic, by which the surveillance can be conducted with a minimum of prior assumptions about the group of occupations/drugs that increase risk, and which adjusts for the multiple testing inherent in the many potential combinations. The method is illustrated using data from the National Center for Health Statistics Multiple Cause of Death Database, looking at the relationship between occupation and death from silicosis.