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James S Koopman

Publications and source records attributed to James S Koopman.

8 recordsLinked to original sources
PubMed2006

Mathematical model comparisons of potential non-typeable Haemophilus influenzae vaccine effects.

Vaccines to prevent acute otitis media (AOM) caused by non-typeable Haemophilus influenzae (NTHi) are under development. Because NTHi is highly variable and colonization rates are high, special vaccine characteristics and trial designs might be needed. We examined in mathematical models the equilibrium NTHi-caused AOM rate given hypothetical vaccines that generated immunity identical to corresponding maximal naturally acquired immunity. Vaccines were examined with single effects and combinations of immunity affecting (1) AOM rates given colonization (pathogenicity), (2) susceptibility to colonization, and (3) contagiousness given colonization. Percent reductions in AOM across all preschool children were (1) 34%, (2) 31%, (3) 9%, (1 and 2) 57%, (2 and 3) 50%, and (1, 2, and 3) 75%. Effects on children in daycare vs. not in daycare were (1) 18 vs. 48%, (2) -1 vs. 57%, (3) 13 vs. 5%, (1 and 2) 30 vs. 79%, (2 and 3) 33 vs. 60%, and (1, 2, and 3) 64 vs. 85%. Pure pathogenicity effects (1 alone) will need to be supplemented by transmission effects. The effects of susceptibility (2 alone) are diminished or negative because children protected against colonization have lower levels of immunity to (1) and (3) than unvaccinated children. For trials to predict population effects, both colonization and AOM outcomes must be studied and all three effects must be evaluated. This need arises because, unlike H. influenzae type B, high NTHi exposure diminishes cumulative vaccine effects and high colonization rates generate rapid accumulation of natural immunity that alters the indirect effects of vaccine immunity on transmission differently by age and daycare status.

Acute Disease↗
PubMed2005

Choosing an appropriate bacterial typing technique for epidemiologic studies.

A wide variety of bacterial typing systems are currently in use that vary greatly with respect to the effort required, cost, reliability and ability to discriminate between bacterial strains. No one technique is optimal for all forms of investigation. We discuss the desired level of discrimination and need for a biologic basis for grouping strains of apparently different types when using bacterial typing techniques for different epidemiologic applications: 1) confirming epidemiologic linkage in outbreak investigations, 2) generating hypotheses about epidemiologic relationships between bacterial strains in the absence of epidemiologic information, and 3) describing the distributions of bacterial types and identifying determinants of those distributions. Inferences made from molecular epidemiologic studies of bacteria depend upon both the typing technique selected and the study design used; thus, choice of typing technique is pivotal for increasing our understanding of the pathogenesis and transmission, and eventual disease prevention.

Journal Article↗
PubMed2005

When to control endemic infections by focusing on high-risk groups.

BACKGROUND: For nontransmissible diseases, decisions between interventions focused on high-risk groups and unfocused interventions can be based on attributable-risk calculations. The assumptions of those calculations, however, are violated for infectious diseases. METHODS: We used deterministic compartmental models of infection transmission having both high- and low-risk groups and both susceptible and infected states to examine intervention effects on endemic infection levels. High risk is generated by increased susceptibility or contagiousness-factors that can be reduced by interventions. Population effects of focused and unfocused interventions are compared at settings where these would be equal if there were no transmission. RESULTS: In the most likely range of mixing between high- and low-risk groups, focused interventions have considerably larger effects than unfocused interventions. At all mixing levels, both interventions have greater effects on infectious than noninfectious diseases because a change in risk factor for one individual alters risk in others. Interventions on contagiousness in high-risk groups have greater effects than comparable interventions on susceptibility. CONCLUSIONS: Risk assessment for infectious disease requires analysis of the population system that is circulating the infection. Vaccine trials on individuals will miss important effects that trials on transmission units will detect. Focusing HIV control on contagiousness factors in high-risk groups will be especially productive.

Communicable Disease Control↗
PubMed2005

Infection transmission science and models.

Infection transmission systems circulate infection through complex contact patterns related to both contact patterns and patterns of factors that affect the risk of transmission given contact. The nonlinear dynamics of infection transmission cause these patterns to make big differences in population infection levels. A science of infection transmission system analysis is needed to focus on those details that affect the control of infection transmission. This science must have a strong theoretical base because there is little chance that a dominantly data based approach not using mechanistic models of transmission will have any predictive value. The theoretical base should be built on linked transmission system models that are focused on making needed inferences for both building the theoretical base and making infection control decisions. The linking of different models is needed for a strategy of inference robustness assessment that is designed to find the model that is simple enough to effectively analyze the transmission system but not so simple that realistic violation of simplifying assumptions will change an inference. Types of models that should be used in such linked analyses include deterministic and stochastic compartmental models, discrete individual models with individual event histories but structured mass action mixing, network models that provide more detail as to who has contact with whom, and intermediate model forms such as correlation models that address some aspects of contact details while preserving the flexibility of deterministic compartmental models to structure mixing and analyze the system. While transmission system science is currently weak in regards both to its data base and its theory base, many things are now coming together that could make this science flourish. On the data side these include greater ability to detect infectious agent sequences in the environment and greater ability to sequence and genetically relate agents identified at different sites in the transmission system. On the theory sides, new model construction and model analysis methods are providing new potential to use the new sources of data. Also new parameter estimation methods provide new potential to combine models and data in effective analytic strategies.

Communicable Diseases↗
PubMed2002

Uropathogenic Escherichia coli are more likely than commensal E. coli to be shared between heterosexual sex partners.

Because uropathogenic Escherichia coli are better adapted than other E. coli to the urethra, periurethra, and vagina, the authors reasoned that uropathogenic E. coli would be more likely than commensal E. coli to be shared between sex partners. In this 1996-1999 Michigan study, the genetic identity of E. coli isolated from 166 women with E. coli urinary tract infection (UTI) and 94 women without UTI and their sex partners was determined by pulsed-field gel electrophoresis. Rectal isolates were considered uropathogenic E. coli if genetically identical to the urinary isolate causing UTI. All eight urinary isolates from men with UTI partners were identical to the E. coli found in the urine or vagina of their sex partner. When the 550 unique rectal E. coli isolates from couples were considered the unit of analysis, E. coli that caused UTI were nine times (odds ratio (OR) = 8.87, 95% confidence interval (CI): 5.41, 14.54) more likely than other E. coli to be shared between sex partners. Sharing occurred twice as frequently (OR = 1.87, 95% CI: 1.13, 3.08) if the E. coli had P pili or if the couples engaged in oral sex (OR = 2.09, 95% CI: 1.09, 4.00). Uropathogenic E. coli are more likely than commensal E. coli to be shared with a current heterosexual sex partner. Both sexual behaviors and a bacterial virulence factor, P pili, modified sharing.

Adaptation, Physiological↗
PubMedDate not supplied

Stochastic effects on endemic infection levels of disseminating versus local contacts.

The effects of two levels of mixing on endemic infection levels are shown to differ for identically conformed deterministic compartmental (DC) and stochastic compartmental (SC) models. Both DC and SC models give similar endemic levels when populations are large, immunity is short lived, and mixing is universal. But local transmissions and/or transient immunity decrease overall population infection levels in SC but not in DC models. DC models also fail to detect the greater effects of eliminating disseminating transmissions in comparison to eliminating local transmissions shown by SC models. These differences in model behavior arise because localities that encounter few infections from distant sites and that have stochastically low infection levels have decreased infection rates while localities with stochastically high levels of infection do not decrease the rate at which they lose infection. At the extreme this generates local stochastic die out with subsequent build up of susceptibility in SC but not DC models. This phenomenon should act upon all endemic infections that have changing geographic or social foci of infection. Neither standard epidemiological investigations nor sufficient-component cause models can capture these effects because they occur in the absence of differences between individuals.

Cryptosporidiosis↗