An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.

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.

The association between multidimensional sleep health and gestational weight gain.

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. OBJECTIVES: This secondary data analysis of the Nulliparous Pregnancy Outcome Study: Monitoring Mothers-to-be Sleep Duration and Continuity Study (n = 745) examined associations between mid-pregnancy sleep health indicators, multidimensional sleep health and gestational weight gain (GWG). METHODS: 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 with empirical thresholds. Multidimensional sleep health was based on sleep profiles derived from latent class analysis and composite score defined as the sum of healthy sleep domains. 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 unhealthy sleep in each domain. The individual sleep domains were associated with a 20%-30% lower risk of low or high GWG. Each additional healthy sleep indicator was associated with a 10% lower risk of low (vs. moderate), but not high, GWG. Participants with late timing, long duration and low efficiency (vs. healthy) profiles had the strongest risk of low GWG (relative risk 1.5, 95% confidence interval 0.9, 2.4). Probabilistic bias analysis suggested that most associations between individual sleep health indicators, sleep health profiles and GWG were biased towards the null. CONCLUSIONS: Future research should determine whether sleep health is an intervention target for healthy GWG.

Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research.

BACKGROUND: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. METHODS: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. RESULTS: Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. CONCLUSIONS: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggest that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

The impact of clinical risk conditions on influenza and pneumonia diagnoses in England: a nationally representative retrospective cohort study, 2010-2019.

The impact of influenza and pneumonia on individuals in clinical risk groups in England has not previously been well characterized. Using nationally representative linked databases (Clinical Practice Research Database (CPRD), Hospital Episode Statistics (HES) and Office for National Statistics (ONS)), we conducted a retrospective cohort study among adults (≥ 18 years) during the 2010/2011-2019/2020 influenza seasons to estimate the incidence of influenza- and pneumonia-diagnosed medical events (general practitioner (GP) diagnoses, hospitalisations and deaths), stratified by age and risk conditions. The study population included a seasonal average of 7.2 million individuals; approximately 32% had ≥1 risk condition, 42% of whom received seasonal influenza vaccines. Medical event incidence rates increased with age, with ~1% of adults aged ≥75 years hospitalized for influenza/pneumonia annually. Among individuals with vs. without risk conditions, GP diagnoses occurred 2-5-fold more frequently and hospitalisations were 7-10-fold more common. Among those with obesity, respiratory, kidney or cardiovascular disorders, hospitalisation were 5-40-fold more common than in individuals with no risk conditions. Though these findings likely underestimate the full burden of influenza, they emphasize the concentration of disease burden in specific age and risk groups and support existing recommendations for influenza vaccination.

Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research.

Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss' kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results: Questions related to the computational environment (mean κ = 0.90, range = 0.90-0.90), analytical software (mean κ = 0.74, range = 0.68-0.82), model description (mean κ = 0.71, range = 0.58-0.84), model implementation (mean κ = 0.68, range = 0.39-0.86), and experimental protocol (mean κ = 0.63, range = 0.58-0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23-0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.

Associations between sleep health and obesity and weight change in adults: The Daily24 Multisite Cohort Study.

OBJECTIVES: To examine cross-sectional and longitudinal associations of individual sleep domains and multidimensional sleep health with current overweight or obesity and 5-year weight change in adults. METHODS: We estimated sleep regularity, quality, timing, onset latency, sleep interruptions, duration, and napping using validated questionnaires. We calculated multidimensional sleep health using a composite score (total number of "good" sleep health indicators) and sleep phenotypes derived from latent class analysis. Logistic regression was used to examine associations between sleep and overweight or obesity. Multinomial regression was used to examine associations between sleep and weight change (gain, loss, or maintenance) over a median of 1.66 years. RESULTS: The sample included 1016 participants with a median age of 52 (IQR = 37-65), who primarily identified as female (78%), White (79%), and college-educated (74%). We identified 3 phenotypes: good, moderate, and poor sleep. More regularity of sleep, sleep quality, and shorter sleep onset latency were associated with 37%, 38%, and 45% lower odds of overweight or obesity, respectively. The addition of each good sleep health dimension was associated with 16% lower adjusted odds of having overweight or obesity. The adjusted odds of overweight or obesity were similar between sleep phenotypes. Sleep, individual or multidimensional sleep health, was not associated with weight change. CONCLUSIONS: Multidimensional sleep health showed cross-sectional, but not longitudinal, associations with overweight or obesity. Future research should advance our understanding of how to assess multidimensional sleep health to understand the relationship between all aspects of sleep health and weight over time.

The association between multidimensional sleep health and gestational weight gain: nuMoM2b Sleep Duration and Continuity Study.

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. Synopsis: Study question: What is the association between mid-pregnancy multidimensional sleep health and gestational weight gain?What's already known?: Sleep is associated with weight and weight gain outside of pregnancyWhat does this study add?: We identified patterns of sleep behaviors associated with an increased risk of low gestational weight gain.

Methods to account for measured and unmeasured confounders in influenza relative vaccine effectiveness studies: A brief review of the literature.

Observational seasonal influenza relative vaccine effectiveness (rVE) studies employ a variety of statistical methods to account for confounding and biases. To better understand the range of methods employed and implications for policy, we conducted a brief literature review. Across 37 included rVE studies, 10 different types of statistical methods were identified, and only eight studies reported methods to detect residual confounding, highlighting the heterogeneous state of the literature. To improve the comparability and credibility of future rVE research, researchers should clearly explain methods and design choices and implement methods to detect and quantify residual confounding.

North to south gradient and local waves of influenza in Chile.

Influenza seasonality is caused by complex interactions between environmental factors, viral mutations, population crowding, and human travel. To date, no studies have estimated the seasonality and latitudinal patterns of seasonal influenza in Chile. We obtained influenza-like illness (ILI) surveillance data from 29 Chilean public health networks to evaluate seasonality using wavelet analysis. We assessed the relationship between the start, peak, and latitude of the ILI epidemics using linear and piecewise regression. To estimate the presence of incoming and outgoing traveling waves (timing vs distance) between networks and to assess the association with population size, we used linear and logistic regression. We found a north to south gradient of influenza and traveling waves that were present in the central, densely populated region of Chile. Our findings suggest that larger populations in central Chile drive seasonal influenza epidemics.

Cutaneous Leishmaniasis during Pregnancy, Preterm Birth, and Neonatal Death: A Case Report.

Cutaneous leishmaniasis (CL) is an emergent public health concern, particularly in tropical and subtropical regions. Reports of pregnancy complications are scarce; however, as the endemic range of CL expands in Iran, there is concern of possible detrimental effects on fetal development amongst infected mothers through placental transmission of the parasite or enhanced maternal immune responses. We herein describe the first known case of persistent anthroponotic CL, plausibly responsible for pregnancy complications, preterm birth, and neonatal death in a healthy Iranian primigravida woman. Diagnosis was based on physical examinations of the lesions on the back of both calves of the patient and laboratory analyses including direct smear, culture, and PCR. During active CL infection, the patient gave birth to a premature female neonate who passed three days post-delivery due to immature lung development and subsequent respiratory distress syndrome. This report highlights the challenges associated with CL infection during pregnancy, exacerbation of lesions, and subsequent complications.

Prevalence of Infection and Co-Infection and Presence of Rickettsial Endosymbionts in Ixodes Scapularis (Acari: Ixodidae) in Connecticut, USA.

Ixodes scapularis is currently known to transmit 7 pathogens responsible for Lyme disease, anaplasmosis, babesiosis, tick-borne relapsing fever, ehrlichiosis, and Powassan encephalitis. Ixodes scapularis can also be colonized by endosymbiotic bacteria including those in the genus of Rickettsia. We screened 459 I. scapularis ticks submitted to the Connecticut Agricultural Experiment Station Tick Testing Laboratory with the objectives to (1) examine differences in infection prevalence of Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi, (2) evaluate whether prevalence of co-infections occur at the same frequency that would be expected based on single infection, and (3) determine the presence of rickettsial endosymbionts in I. scapularis. The prevalence of infection in I. scapularis was highest with Bo. burgdorferi sensu lato (nymph = 45.8%; female = 47.0%), followed by A. phagocytophilum (nymph = 4.0%; female = 6.9%), Ba. microti (nymph = 5.7%; female = 4.7%), and Bo. miyamotoi (nymph = 0%; female = 7.3%). We also identified rickettsial endosymbionts in 93.3% of I. scapularis. Nymphs were significantly more likely to be infected with Bo. burgdorferi if they were infected with Ba. microti, whereas adult females were significantly more likely to be infected with Bo. burgdorferi if they were infected with A. phagocytophilum. Our study suggests that the infection prevalence of Bo. burgdorferi is not independent of other co-circulating pathogens and that there is a substantially higher infection of Bo. miyamotoi in I. scapularis females compared with nymphs in this study. High prevalence of infection and co-infection with multiple pathogens in I. scapularis highlights the public health consequences in Connecticut, a state endemic for Lyme and other tick-borne diseases.

Employer-mandated obstructive sleep apnea treatment and healthcare cost savings among truckers.

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.

Host tolerance and resistance to parasitic nest flies differs between two wild bird species.

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.