Hosts have developed and evolved defense strategies to limit parasite damage. Hosts can reduce the damage that parasites cause by decreasing parasite fitness (resistance) or without affecting parasite fitness (tolerance). Because a parasite species can infect multiple host species, determining the effect of the parasite on these hosts and identifying host defense strategies can have important implications for multi‐host–parasite dynamics. Over 2 years, we experimentally manipulated parasitic flies (Protocalliphora sialia) in the nests of tree swallows (Tachycineta bicolor) and eastern bluebirds (Sialia sialis). We then determined the effects of the parasites on the survival of nestlings and compared defense strategies between host species. We compared resistance between host species by quantifying parasite densities (number of parasites per gram of host) and measured nestling antibody levels as a mechanism of resistance. We quantified tolerance by determining the relationship between parasite density and nestling survival and blood loss by measuring hemoglobin levels (as a proxy of blood recovery) and nestling provisioning rates (as a proxy of parental compensation for resources lost to the parasite) as potential mechanisms of tolerance. For bluebirds, parasite density was twice as high as for swallows. Both host species were tolerant to the effects of P. sialia on nestling survival at their respective parasite loads but neither species were tolerant to the blood loss to the parasite. However, swallows were more resistant to P. sialia compared to bluebirds, which was likely related to the higher antibody‐mediated immune response in swallow nestlings. Neither blood recovery nor parental compensation were mechanisms of tolerance. Overall, these results suggest that bluebirds and swallows are both tolerant of their respective parasite loads but swallows are more resistant to the parasites. These results demonstrate that different host species have evolved similar and different defenses against the same species of parasite.

Objective To evaluate the effect of an employer-mandated obstructive sleep apnea (OSA) diagnosis and treatment program on non-OSA-program trucker medical insurance claim costs. Methods Retrospective cohort analysis; cohorts constructed by matching (randomly, with replacement) Screen-positive Controls (drivers with insurance screened as likely to have OSA, but not yet diagnosed) with Diagnosed drivers (n = 1,516; cases = 1,224, OSA Negatives = 292), on two factors affecting exposure to medical claims: experience level at hire and weeks of job tenure at the Diagnosed driver’s polysomnogram (PSG) date (the “matching date”). All cases received auto-adjusting positive airway pressure (APAP) treatment and were grouped by objective treatment adherence data: any “Positive Adherence” (n = 932) versus “No Adherence” (n = 292). Bootstrap resampling produced a difference-in-differences estimate of aggregate non-OSA-program medical insurance claim cost savings for 100 Diagnosed drivers as compared to 100 Screen-positive Controls before and after the PSG/matching date, over an 18-month period. A two-part multivariate statistical model was used to set exposures and demographics/anthropometrics equal across sub-groups, and to generate a difference-in-differences comparison across periods that identified the effect of OSA treatment on per-member per-month (PMPM) costs of an individual driver, separately from cost differences associated with adherence choice. Results Eighteen-month non-OSA-program medical claim costs savings from diagnosing (and treating as required) 100 Screen-positive Controls: $153,042 (95% CI: −$5,352, $330,525). Model-estimated effect of treatment on those adhering to APAP: −$441 PMPM (95% CI: −$861, −$21). Conclusions Results suggest a carrier-based mandatory OSA program generates substantial savings in non-OSA-program medical insurance claim costs.

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Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.

Background: Although poor sleep health is associated with weight gain and obesity in the non-pregnant population, research on the impact of sleep health on weight change among pregnant people using a multidimensional sleep-health framework is needed. This study examined associations among mid-pregnancy sleep health indicators, multidimensional sleep health, and gestational weight gain (GWG). Methods: We conducted a secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n=745). Indicators of individual sleep domains (i.e., regularity, nap duration, timing, efficiency, and duration) were assessed via actigraphy between 16 and 21 weeks of gestation. We defined healthy sleep in each domain based on empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis. Total GWG, the difference between self-reported pre-pregnancy weight and the last measured weight before delivery, was converted to z-scores using gestational age- and BMI-specific charts. GWG was defined as low (<-1 SD), moderate (-1 or +1 SD), and high (>+1 SD). Results: Nearly 50% of the participants had a healthy sleep profile (i.e., healthy sleep in most domains), whereas others had a sleep profile defined as having varying degrees of poor health in each domain. While indicators of individual sleep domains were not associated with GWG, multidimensional sleep health was related to low and high GWG. Participants with a sleep profile characterized as having low efficiency, late timing, and long sleep duration (vs. healthy sleep profile) had a higher risk (RR 1.7; 95% CI 1.0, 3.1) of low GWG a lower risk of high GWG (RR 0.5 95% CI 0.2, 1.1) (vs. moderate GWG). Conclusions: Multidimensional sleep health was more strongly associated with GWG than individual sleep domains. Future research should determine whether sleep health is a valuable intervention target for optimizing GWG.