Modeling the Effects of Reservoir Competence Decay and Demographic Turnover in Lyme Disease Ecology

Lyme disease risk is related to the abundance of infected nymphal ticks, which in turn depends on the abundance and reservoir competence of wild hosts. Reservoir competence of a host (i.e., probability that an infected host will infect a feeding vector) often declines over time after inoculation, and small mammalian reservoirs typically undergo rapid population growth during the period when vector ticks feed. These processes can affect disease risk in the context of site-specific tick abundance and host community composition. We modeled the effects of reservoir decay and host demographic turnover on Lyme disease risk using a simple yearly difference equation model and a more realistic simulation incorporating seasonal dynamics of ticks and hosts. Both reservoir decay and demographic turnover caused (1) specific infectivity (proportion infected 3 reservoir competence) of host populations to vary with host community composition, (2) tick infection prevalence and the specific infectivity of reservoirs to be highly sensitive to the abundance of questing nymphs, and (3) specific infectivity and the infection prevalence of ticks to decrease at high host densities. Reservoir competence decay had similar effects in both model formulations, but host turnover had less effect than reservoir decay in the seasonal model. In general, exponential reservoir decay and abrupt loss of reservoir competence had similar effects, although exponential decay caused greater sensitivity to tick density and host community composition. Reservoir decay may explain the observed variability in published field measurements of reservoir competence of a host species. Our results illuminate mechanisms by which host diversity can dilute the impact of a highly competent reservoir and suggest that management to reduce nymphal tick abundance may reap an added benefit by reducing nymphal infection prevalence

Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room

Background: Diagnostic errors occur frequently, especially in the emergency room. Estimates about the consequences of diagnostic error vary widely and little is known about the factors predicting error. Our objectives thus was to determine the rate of discrepancy between diagnoses at hospital admission and discharge in patients presenting through the emergency room, the discrepancies’ consequences, and factors predicting them. Methods: Prospective observational clinical study combined with a survey in a University-affiliated tertiary care hospital. Patients’ hospital discharge diagnosis was compared with the diagnosis at hospital admittance through the emergency room and classified as similar or discrepant according to a predefined scheme by two independent expert raters. Generalized linear mixed-effects models were used to estimate the effect of diagnostic discrepancy on mortality and length of hospital stay and to determine whether characteristics of patients, diagnosing physicians, and context predicted diagnostic discrepancy. Results: 755 consecutive patients (322 [42.7%] female; mean age 65.14 years) were included. The discharge diagnosis differed substantially from the admittance diagnosis in 12.3% of cases. Diagnostic discrepancy was associated with a longer hospital stay (mean 10.29 vs. 6.90 days; Cohen’s d 0.47; 95% confidence interval 0.26 to 0.70; P = 0.002) and increased patient mortality (8 (8.60%) vs. 25(3.78%); OR 2.40; 95% CI 1.05 to 5.5 P = 0.038). A factor available at admittance that predicted diagnostic discrepancy was the diagnosing physician’s assessment that the patient presented atypically for the diagnosis assigned (OR 3.04; 95% CI 1.33–6.96; P = 0.009). Conclusions: Diagnostic discrepancies are a relevant healthcare problem in patients admitted through the emergency room because they occur in every ninth patient and are associated with increased in-hospital mortality. Discrepancies are not readily predictable by fixed patient or physician characteristics; attention should focus on context

What Is the Best Predictor of Annual Lyme Disease Incidence: Weather, Mice, or Acorns?

Predicting fluctuations in annual risk of Lyme disease would be useful in focusing public health efforts. However, several competing hypotheses have been proposed that point to weather variables, acorn production, or mouse abundance as important predictors of Lyme disease risk. We compared the ability of acorn production, mouse density, and four relevant weather variables to predict annual Lyme disease incidence (detrended) between 1992 and 2002 for Dutchess County, New York, and seven states in the northeastern United States. Acorn production and mouse abundance measured in Dutchess County were the strongest predictors (r ≥ 0.78) of Dutchess County Lyme disease incidence, but the increase in mouse abundance from 1991 to 1992 was contrary to a decrease in Lyme disease in the following years. The Palmer Hydrologic Drought Index (PHDI) was a significant positive predictor of Lyme disease incidence two years later for three states (0.58 ≤ r ≤ 0.88), but summer precipitation was generally negatively correlated with Lyme disease incidence the next year (-0.79 ≤ r ≤ 0.02). Mean temperatures for the prior winter or summer showed weak or inconsistent correlations with Lyme disease incidence. In four states, no variable was a statistically significant predictor of Lyme disease incidence. Synchrony in Lyme disease incidence between pairs of states was not significantly concordant with synchrony in any weather variable that we examined (0.02 ≤ r ≤ 0.21). We found that acorns and mice were strong predictors of Dutchess County Lyme disease incidence, but their predictive power appeared to be weaker spatially. Moreover, evidence was weak for causal relationships between Lyme disease incidence and the weather variables that we tested. Reliable prediction of Lyme disease incidence may require the identification of new predictors or combinations of biotic and abiotic predictors and may be limited to local scales

Limited Dispersal and Heterogeneous Predation Risk Synergistically Enhance Persistence of Rare Prey

White-footed mice prey on gypsy moth pupae while foraging for other, more abundant food. Mice appear capable of locally extirpating moths since mice exert high predation pressure on sparse pupae and are numerically decoupled from moth populations. Nevertheless, during 23 years of monitoring, moths persisted at scales .1 ha despite frequent extinctions at smaller spatial scales. We hypothesized that spatially heterogeneous intensity in mouse foraging and/or limited moth dispersal might allow moth persistence. Using a spatially explicit, individual-based, empirically parameterized model, we show that neither spatially heterogeneous predation by mice, nor limited moth dispersal alone allows moth persistence at typical mouse densities. However, synergy between both factors allows moth population persistence at naturally occurring mouse densities. For example, in models with 40 mice/ha, both limited moth dispersal with spatially homogeneous predation risk and spatially heterogeneous predation risk with unlimited moth dispersal had a 0% chance of moth persistence, but the combination of limited dispersal and heterogeneous predation risk resulted in a ~75% chance of moth persistence. Furthermore, both for limited moth dispersal with spatially homogeneous predation risk and for spatially heterogeneous predation risk with unlimited moth dispersal, moth persistence was only guaranteed at very low mouse densities, while the combination of limited moth dispersal with heterogeneous predation guaranteed moth persistence within a broad range of mouse densities. The findings illustrate a novel mechanism of ‘‘spatial selection and satiation’’ that can enhance rare species persistence under intense incidental predation by generalist predators

Spatial selection and inheritance: applying evolutionary concepts to population dynamics in heterogeneous space.

Organisms in highly suitable sites generally produce more offspring, and offspring can inherit this suitability by not dispersing far. This combination of spatial selection and spatial inheritance acts to bias the distribution of organisms toward suitable sites and thereby increase mean fitness (i.e., per capita population increase). Thus, population growth rates in heterogeneous space change over time by a process conceptually analogous to evolution by natural selection, opening avenues for theoretical cross-pollination between evolutionary biology and ecology. We operationally define spatial inheritance and spatial selective differential and then combine these two factors in a modification of the breeder\u27s equation, derived from simple models of population growth in heterogeneous space. The modified breeder\u27s equation yields a conservative criterion for persistence in hostile environments estimable from field measurements. We apply this framework for understanding gypsy moth population persistence amidst abundant predators and find that the predictions of the modified breeder\u27s equation match initial changes in population growth rate in independent simulation output. The analogy between spatial dynamics and natural selection conceptually links ecology and evolution, provides a spatially implicit framework for modeling spatial population dynamics, and represents an important null model for studying habitat selection

Differential diagnosis checklists reduce diagnostic error differentially: a randomized experiment

Introduction Wrong and missed diagnoses contribute substantially to medical error. Can a prompt to generate alternative diagnoses (prompt) or a differential diagnosis checklist (DDXC) increase diagnostic accuracy? How do these interventions affect the diagnostic process and self-monitoring? Methods Advanced medical students (N = 90) were randomly assigned to one of four conditions to complete six computer-based patient cases: group 1 (prompt) was instructed to write down all diagnoses they considered while acquiring diagnostic test results and to finally rank them. Groups 2 and 3 received the same instruction plus a list of 17 differential diagnoses for the chief complaint of the patient. For half of the cases, the DDXC contained the correct diagnosis (DDXC+), and for the other half, it did not (DDXC−; counterbalanced). Group 4 (control) was only instructed to indicate their final diagnosis. Mixed-effects models were used to analyse results. Results Students using a DDXC that contained the correct diagnosis had better diagnostic accuracy, mean (standard deviation), 0.75 (0.44), compared to controls without a checklist, 0.49 (0.50), P < 0.001, but those using a DDXC that did not contain the correct diagnosis did slightly worse, 0.43 (0.50), P = 0.602. The number and relevance of diagnostic tests acquired were not affected by condition, nor was self-monitoring. However, participants spent more time on a case in the DDXC−, 4:20 min (2:36), P ≤ 0.001, and DDXC+ condition, 3:52 min (2:09), than in the control condition, 2:59 min (1:44), P ≤ 0.001. Discussion Being provided a list of possible diagnoses improves diagnostic accuracy compared with a prompt to create a differential diagnosis list, if the provided list contains the correct diagnosis. However, being provided a diagnosis list without the correct diagnosis did not improve and might have slightly reduced diagnostic accuracy. Interventions neither affected information gathering nor self-monitoring

Measurement of mutual inductance from frequency dependence of impedance of AC coupled circuit using digital dual-phase lock-in amplifier

We present a simple method to determine the mutual inductance $M$ between two coils in a coupled AC circuit by using a digital dual-phase lock-in amplifier. The frequency dependence of the real and imaginary parts is measured as the coupling constant is changed. The mutual inductance $M$ decreases as the distance $d$ between the centers of coils is increased. We show that the coupling constant is proportional to $d^{-n}$ with an exponent $n$ ($\approx$ 3). This coupling is similar to that of two magnetic moments coupled through a dipole-dipole interaction.Comment: 9 pages, 10 figures, Fig.1 is corrected, figures in png files, short version is published in Am. J. Phys. 76, (2008) 12