Spatial inflection and memory for direction in Acazulco Otomi

Many languages have developed a specialized tool for coding spatial background aspects of events: associated motion morphology. This sparsely investigated verb inflection allows speakers to specify that the situation described by a verb takes place against the background of a motion event, as in “sing (while coming)”. Associated-motion systems typically include deictic information, and when verb inflection requires distinctions between motion in different directions, a thinking-for-speaking account would predict cognitive consequences in the shape of heightened memory for direction. To evaluate this hypothesis, we compare encoding of and memory for direction in an endangered Otopamean language, Acazulco Otomí (Mexico). First, we examine diversity and frequency in the use of associated-motion inflection in pilgrim narratives. Then, we investigate the potential cognitive correlates with a psycholinguistic recognition-memory experiment measuring change-detection performance. Linguistic encoding of background direction was found to support memory for direction, but the sample size was small, and the experiment further indicated that both the associated-motion inflection and its corresponding attention patterns are in a process of dissolution. This echoes findings in Arrernte and Mojeño Trinitario, and we discuss why associated motion might be an especially vulnerable category in language-endangerment contexts

Prevalence and risk factors of community-associated methicillin-resistant carriage in Asia-Pacific region from 2000 to 2016:a systematic review and meta-analysis

Objective: Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is an emerging global public health threat. In response to a highlighted strategic priority of the World Health Organization Global Action Plan on Antimicrobial Resistance, to "strengthen the knowledge and evidence base through surveillance and research", we synthesized published articles to estimate CA-MRSA carriage prevalence in the Asia-Pacific region. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO CRD:42017067399). We searched MEDLINE, EMBASE, and PubMed for articles published from 1 January 2000 to 19 May 2017, which reported CA-MRSA carriage (defined as either colonization or infection) in Asia-Pacific region from 2000 to 2016. Studies were stratified according to settings (community or hospital where CA-MRSA was isolated) and study populations (general public or subpopulations with specified characteristics). Ranges of CA-MRSA carriage prevalence were reported for study groups. Results: In total, 152 studies were identified. Large diversity was observed among studies in most study groups. In community-level studies, the CA-MRSA carriage prevalence among the general public ranged from 0% to 23.5%, whereas that ranged from 0.7% to 10.4% in hospital settings. From community-level studies, countries with the highest prevalence were India (16.5%-23.5%), followed by Vietnam (7.9%) and Taiwan (3.5%-3.8%). Children aged ≤6 (range: 0.5%-40.3%) and household members of CA-MRSA carriers (range: 13.0%-26.4%) are subgroups without specific health conditions but with much higher CA-MRSA carriage when compared to the general population. Conclusion: Our CA-MRSA prevalence estimates serve as the baseline for future national and international surveillance. The ranges of prevalence and characteristics associated with CA-MRSA carriage can inform health authorities to formulate infection control policies for high-risk subgroups. Future studies should explore the heterogeneities in CA-MRSA carriage prevalence among subgroups and countries to clarify the predominant transmission mechanisms in Asia-Pacific and other regions

A comparison of hemagglutination inhibition and neutralization assays for characterizing immunity to seasonal influenza A

SummaryBackgroundSerum antibody to influenza can be used to identify past exposure and measure current immune status. The two most common methods for measuring this are the hemagglutination inhibition assay (HI) and the viral neutralization assay (NT), which have not been systematically compared for a large number of influenza viruses.Methods151 study participants from near Guangzhou, China were enrolled in 2009 and provided serum. HI and NT assays were performed for 12 historic and recently circulating strains of seasonal influenza A. We compared titers using Spearman correlation and fit models to predict NT using HI results.ResultsWe observed high positive mean correlation between HI and NT assays (Spearman's rank correlation, rho=0.86) across all strains. Correlation was highest within subtypes and within close proximity in time. Overall, an HI=20 corresponded to NT=10, and HI=40 corresponded to NT=20. Linear regression of log(NT) on log(HI) was statistically significant, with age modifying this relationship. Strain-specific area under a curve (AUC) indicated good accuracy (>80%) for predicting NT with HI.ConclusionsWhile we found high overall correspondence of titers between NT and HI assays for seasonal influenza A, no exact equivalence between assays could be determined. This was further complicated by correspondence between titers changing with age. These findings support generalized comparison of results between assays and give further support for use of the hemagglutination inhibition assay over the more resource intensive viral neutralization assay for seasonal influenza A, though attention should be given to the effect of age on these assays

Modelling the Proportion of Influenza Infections within Households during Pandemic and Non-Pandemic Years

Background: The key epidemiological difference between pandemic and seasonal influenza is that the population is largely susceptible during a pandemic, whereas, during non-pandemic seasons a level of immunity exists. The population-level efficacy of household-based mitigation strategies depends on the proportion of infections that occur within households. In general, mitigation measures such as isolation and quarantine are more effective at the population level if the proportion of household transmission is low. Methods/Results: We calculated the proportion of infections within households during pandemic years compared with non-pandemic years using a deterministic model of household transmission in which all combinations of household size and individual infection states were enumerated explicitly. We found that the proportion of infections that occur within households was only partially influenced by the hazard h of infection within household relative to the hazard of infection outside the household, especially for small basic reproductive numbers. During pandemics, the number of within-household infections was lower than one might expect for a given h because many of the susceptible individuals were infected from the community and the number of susceptible individuals within household was thus depleted rapidly. In addition, we found that for the value of h at which 30% of infections occur within households during non-pandemic years, a similar 31% of infections occur within households during pandemic years. Interpretation: We suggest that a trade off between the community force of infection and the number of susceptible individuals in a household explains an apparent invariance in the proportion of infections that occur in households in our model. During a pandemic, although there are more susceptible individuals in a household, the community force of infection is very high. However, during non-pandemic years, the force of infection is much lower but there are fewer susceptible individuals within the household. © 2011 Kwok et al.published_or_final_versio

Models of directly transmitted respiratory pathogens in hospitals and households

published_or_final_versionCommunity MedicineDoctoralDoctor of Philosoph

A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong

Abstract Background Accurately estimating elderly patients’ rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants. Methods A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution’s expected count. tZIP was externally validated with a retrospective cohort’s rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model’s prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator’s marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses. Results The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator’s contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status. Conclusions A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients’ rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time

Using models to identify routes of nosocomial infection: a large hospital outbreak of SARS in Hong Kong

Two factors dominated the epidemiology of severe acute respiratory syndrome (SARS) during the 2002–2003 global outbreak, namely super-spreading events (SSE) and hospital infections. Although both factors were important during the first and the largest hospital outbreak in Hong Kong, the relative importance of different routes of infection has not yet been quantified. We estimated the parameters of a novel mathematical model of hospital infection using SARS episode data. These estimates described levels of transmission between the index super-spreader, staff and patients, and were used to compare three plausible hypotheses. The broadest of the supported hypotheses ascribes the initial surge in cases to a single super-spreading individual and suggests that the per capita risk of infection to patients increased approximately one month after the start of the outbreak. Our estimate for the number of cases caused by the SSE is substantially lower than the previously reported values, which were mostly based on self-reported exposure information. This discrepancy suggests that the early identification of the index case as a super-spreader might have led to biased contact tracing, resulting in too few cases being attributed to staff-to-staff transmission. We propose that in future outbreaks of SARS or other directly transmissible respiratory pathogens, simple mathematical models could be used to validate preliminary conclusions concerning the relative importance of different routes of transmission with important implications for infection control

The within-household transmission percentages in non-pandemic years and the corresponding percentage difference from that in pandemic years.

<p>The contour lines show the percentages of within-household transmission in non-pandemic years, and the heat chart shows the differences between percentages of within-household transmission in non-pandemic years and pandemic years under different combinations of and . For example, if and , then the percentage of within-household transmission in a non-pandemic year would be 45% and the difference between the percentage of within household transmission in non-pandemic years and pandemic years would be approximately 2.5 to 3.0%.</p