Multiple social and environmental factors affect wildland fire response of full or less-than-full suppression.

Wildland fire incident commanders make wildfire response decisions within an increasingly complex socio-environmental context. Threats to human safety and property, along with public pressures and agency cultures, often lead commanders to emphasize full suppression. However, commanders may use less-than-full suppression to enhance responder safety, reduce firefighting costs, and encourage beneficial effects of fire. This study asks: what management, socioeconomic, environmental, and fire behavior characteristics are associated with full suppression and the less-than-full suppression methods of point-zone protection, confinement/containment, and maintain/monitor? We analyzed incident report data from 374 wildfires in the United States northern Rocky Mountains between 2008 and 2013. Regression models showed that full suppression was most strongly associated with higher housing density and earlier dates in the calendar year, along with non-federal land jurisdiction, regional and national incident management teams, human-caused ignitions, low fire-growth potential, and greater fire size. Interviews with commanders provided decision-making context for these regression results. Future efforts to encourage less-than-full suppression should address the complex management context, in addition to the biophysical context, of fire response.

Cochlear Function in Individuals with and without Spontaneous Otoacoustic Emissions.

PURPOSE: This study investigated the status of spontaneous otoacoustic emissions (SOAEs) on cochlear function in a cohort of male/female participants with a wide age range. It examined whether there was a correlation between the presence of SOAEs and measurements of transient evoked otoacoustic emissions (TEOAEs), distortion product otoacoustic emissions (DPOAEs), SOAEs and extended high-frequency (EHF) hearing thresholds. METHODS: 463 participants (222 male, 241 female; age range 20-59 years) with pure-tone thresholds ≤25 dB HL for octave frequencies of 500-8000 Hz were included in the study, divided into three age groups (20-29, 30-39, and 40-59 years). Evaluations included EHF (9000-16,000 Hz) hearing thresholds and TEOAE, DPOAE and SOAE measures. RESULTS: Multiple regression models showed that participants with SOAEs had larger expected amplitudes and signal-to-noise ratios (SNRs) for TEOAE and DPOAE responses than participants without SOAEs, holding gender and age variables constant. Spearman correlation tests identified deterioration in TEOAE and DPOAE amplitudes and SNRs, and EHF hearing thresholds with age in participants without SOAEs. Among participants with SOAEs, no significant decreases in TEOAE and DPOAE measures were shown in participants with older age. Nonetheless, as expected, EHF hearing thresholds did become worse with age, with or without SOAEs. CONCLUSIONS: Participants with identifiable SOAEs had greater TEOAE and DPOAE amplitudes and SNRs than participants without SOAEs. SOAEs appear to be a useful marker of cochlear health in adults.

Prediction model on disease recurrence for low risk resected stage I lung adenocarcinoma.

BACKGROUND AND OBJECTIVE: Although stage I non-small cell lung carcinoma (NSCLC) typically carries a good prognosis following complete resection, early disease recurrence can occur. An accurate survival prediction model would help refine a follow-up strategy and personalize future adjuvant therapy. We developed a post-operative prediction model based on readily available clinical information for patients with stage I adenocarcinoma. METHODS: We retrospectively studied the disease-free survival (DFS) of 408 patients with pathologically confirmed low-risk stage I adenocarcinoma of lung who underwent curative resection from 2013 to 2017. A tree-based method was employed to partition the cohort into subgroups with distinct DFS outcome and stepwise risk ratio. These covariates were included in multivariate analysis to build a scoring system to predict disease recurrence. The model was subsequently validated using a 2011-2012 cohort. RESULTS: Non-smoker status, stage IA disease, epidermal-growth factor receptor mutants and female gender were associated with better DFS. Multivariate analysis identified smoking status, disease stage and gender as factors necessary for the scoring system and yielded 3 distinct risk groups for DFS [99.4 (95% CI 78.3-125.3), 62.9 (95% CI 48.2-82.0), 33.7 (95% CI 24.6-46.1) months, p < 0.005]. External validation yielded an area under the curve by receiver operating characteristic analysis of 0.863 (95% CI 0.755-0.972). CONCLUSION: The model could categorize post-operative patients using readily available clinical information, and may help personalize a follow-up strategy and future adjuvant therapy.

Changes in Etiology and Clinical Outcomes of Pleural empyema during the COVID-19 Pandemic.

Healthcare-seeking behavior changed during the COVID-19 pandemic and might alter the epidemiology of pleural empyema. In this study, the incidence, etiology and outcomes of patients admitted for pleural empyema in Hong Kong in the pre-COVID-19 (January 2015-December 2019) and post-COVID-19 (January 2020-June 2022) periods were compared. Overall, Streptococcus pneumoniae was the predominant organism in <18-year-old patients, while Streptococcus anginosus, anaerobes and polymicrobial infections were more frequent in adults. In the post-COVID-19 period, a marked decline in the incidence of pleural empyema in children was observed (pre-COVID-19, 18.4 ± 4.8 vs. post-COVID-19, 2.0 ± 2.9 cases per year, p = 0.036), while the incidence in adults remained similar (pre-COVID-19, 189.0 ± 17.2 vs. post-COVID-19, 198.4 ± 5.0 cases per year; p = 0.23). In the post-COVID-19 period, polymicrobial etiology increased (OR 11.37, p < 0.0001), while S. pneumoniae etiology decreased (OR 0.073, p < 0.001). In multivariate analysis, clinical outcomes (length of stay, ICU admission, use of intrapleural fibrinolytic therapy, surgical intervention, death) were not significantly different in pre- and post-COVID-19 periods. In conclusion, an increase in polymicrobial pleural empyema was observed during the pandemic. We postulate that this is related to the delayed presentation of pneumonia to hospitals.

On generalized latent factor modeling and inference for high-dimensional binomial data.

We explore a hierarchical generalized latent factor model for discrete and bounded response variables and in particular, binomial responses. Specifically, we develop a novel two-step estimation procedure and the corresponding statistical inference that is computationally efficient and scalable for the high dimension in terms of both the number of subjects and the number of features per subject. We also establish the validity of the estimation procedure, particularly the asymptotic properties of the estimated effect size and the latent structure, as well as the estimated number of latent factors. The results are corroborated by a simulation study and for illustration, the proposed methodology is applied to analyze a dataset in a gene-environment association study.

ABO blood group and cochlear function: evidence from a large sample size study.

OBJECTIVE: The present study investigated the effect of blood group on cochlear function in a large participant sample across different age groups. The study hypothesis was that participants with blood group O would show relatively reduced cochlear function as reflected in otoacoustic emission (OAE) measures. DESIGN: Data were collected from transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), DPOAE input/output (I/O) function, and spontaneous otoacoustic emission (SOAE) recordings. STUDY SAMPLE: Four hundred and sixty-three normal hearing adults aged 20-59 years among the four ABO blood groups participated in the study. RESULTS: TEOAE and DPOAE amplitudes did not reveal significant differences for participants with blood group O compared with participants with non-O blood groups. No significant differences in I/O function categories were found among participants with different blood groups. SOAE prevalence was also not significantly different across blood groups. However, previously reported age and gender differences for OAE variables were confirmed. CONCLUSIONS: Participants with blood group O were not found to have significantly reduced cochlear function, based on OAE measures. Results from the current study do not support the hypothesis that normal hearing individuals with different ABO blood groups differ in level of cochlear function.

A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity.

BACKGROUND: Amiodarone is one of the most commonly used anti-arrhythmic agents. Amiodarone pulmonary toxicity is a potentially fatal adverse effect associated with amiodarone use. Previous studies on the epidemiology and risk factors for amiodarone pulmonary toxicity showed diverse results. METHODS: A multicenter retrospective cohort study was conducted to identify clinic-epidemiologic markers associated with amiodarone pulmonary toxicity for development of a prediction rule. Patients taking amiodarone who were managed in 3 centres in Hong Kong from 2005 to 2015 were included in this study. Penalized logistic regression was used to model the outcome as it is rare. RESULTS: A total of 34 cases with amiodarone pulmonary toxicity were identified among 1786 patients taking amiodarone for at least 90 days from 2005 to 2015. The incidence of amiodarone pulmonary toxicity was estimated to be 1.9%. The risk factors for amiodarone pulmonary toxicity included advanced age (OR 1.047, 95% CI 1.010-1.085, p = 0.013), ventricular arrhythmia (OR 2.703, 95% CI 1.053-6.935, p = 0.039), underlying lung disease (OR 2.511, 95% CI 1.146-5.501, p = 0.021) and cumulative dose of amiodarone (OR 4.762, 95% CI 1.310-17.309 p = 0.018). CONCLUSIONS: The incidence of amiodarone pulmonary toxicity in Chinese patients in Hong Kong is estimated to be 1.9% in this study. Age, underlying lung disease, ventricular arrhythmia and cumulative dose of amiodarone are associated with the development of amiodarone pulmonary toxicity. A prediction rule was developed to inform the risk of developing amiodarone pulmonary toxicity.

Nanoparticle tracking analysis and statistical mixture distribution analysis to quantify nanoparticle-vesicle binding.

Nanoparticle tracking analysis (NTA) is a single particle tracking technique that in principle provides a more direct measure of particle size distribution compared to dynamic light scattering (DLS). Here, we demonstrate how statistical mixture distribution analysis can be used in combination with NTA to quantitatively characterize the amount and extent of particle binding in a mixture of nanomaterials. The combined approach is used to study the binding of gold nanoparticles to two types of phospholipid vesicles, those containing and lacking the model ion channel peptide gramicidin A. This model system serves as both a proof of concept for the method and a demonstration of the utility of the approach in studying nano-bio interactions. Two diffusional models (Stokes-Einstein and Kirkwood-Riseman) were compared in the determination of particle size, extent of binding, and nanoparticle:vesicle binding ratios for each vesicle type. The combination of NTA and statistical mixture distributions is shown to be a useful method for quantitative assessment of the extent of binding between particles and determination of binding ratios.

Global trends in cross-cultural endorsement of social mobility: Evidence from 167 countries.

The American Dream-the perception that upward social mobility depends on effort-is a central cultural ethos in the United States. The belief in upward social mobility is not unique to Americans, and cultural groups across the world endorse it to varying degrees. The current study aims to examine cross-cultural trends in perceived mobility and to test possible mechanisms that may explain changes in perceived mobility. Using a dataset of over 1.4 million participants across 167 countries from 2005 to 2019, we comprehensively document cultural variations in perceived mobility. Citizens in Bhutan, Qatar, and Uzbekistan reported the highest levels of perceived mobility, and the United States ranked 107. We further examined the trajectories of perceived mobility across a 15-year timespan and found rapid declines in perceived mobility in countries experiencing sociopolitical crises (e.g., in Syria and Hong Kong). Multilevel analyses revealed high-income individuals are 32% (95% confidence interval, CI [24%, 39%]) more likely to perceive mobility than low-income individuals, and the level of disparity did not decrease over time. Preregistered time-series analyses showed education privatization and economic condition Granger-cause perceived mobility, but these temporal associations showed heterogeneity across countries. Overall, we performed the world's largest global monitor of perceived mobility and discussed how the cultural value of perceived mobility unfolds in social, economic, and political contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Invasive pneumococcal disease, pneumococcal pneumonia and all-cause pneumonia in Hong Kong during the COVID-19 pandemic compared with the preceding 5 years: a retrospective observational study.

OBJECTIVES: To compare the incidence and severity of invasive pneumococcal diseases (IPDs), pneumococcal pneumonia and all-cause pneumonia during the COVID-19 pandemic period with universal masking and social distancing with that of previous 5 years. DESIGN: Retrospective observational study on incidence of IPDs, pneumococcal pneumonia and all-cause pneumonia between January 2015-December 2019 and March 2020-March 2021. January-February 2020 was excluded from analysis as it was treated as a transitional period between normal time and pandemic. SETTING: Episode-based data by retrieval of hospitalisation records from the Hospital Authority's territory-wide electronic medical record database in Hong Kong. PARTICIPANTS: Hospitalised patients with IPD (n=742), pneumococcal pneumonia (n=2163) and all-cause pneumonia (including COVID-19 pneumonia, n=453 999) aged 18 years or above. Control diagnoses were included to assess confounding from health-seeking behaviours. PRIMARY AND SECONDARY OUTCOMES: Primary outcome is the incidence of diseases between two periods. Secondary outcomes include disease severity surrogated by length of stay and mortality. RESULTS: Monthly average number of IPD, pneumococcal pneumonia and all-cause pneumonia hospitalisation significantly decreased by 88.9% (95% CI 79.8% to 98.0%, p<0.0005), 72.5% (95% CI 65.9% to 79.1%, p<0.0005) and 17.5% (95% CI 16.8% to 18.2%, p<0.0005), respectively. Changes in trend from January 2015-December 2019 to March 2020-March 2021 were -70% (95% CI -87% to -35%, p=0.0025), -43% (95% CI -59% to -19%, p=0.0014) and -11% (95% CI -13% to -10%, p<0.0005), respectively. Length of stay for IPD and pneumococcal pneumonia episodes were insignificantly different in the two periods. No reductions in hospitalisations for control diagnoses were observed. CONCLUSIONS: Incidence of IPD, pneumococcal pneumonia and all-cause pneumonia decreased during the COVID-19 pandemic. This was observed with universal masking and social distancing. We postulated this is related to reduced transmission of respiratory viruses and bacteria.

Modelling the impact of travel restrictions on COVID-19 cases in Hong Kong in early 2020.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) led to pandemic that affected almost all countries in the world. Many countries have implemented border restriction as a public health measure to limit local outbreak. However, there is inadequate scientific data to support such a practice, especially in the presence of an established local transmission of the disease. OBJECTIVE: To apply a metapopulation Susceptible-Exposed-Infectious-Recovered (SEIR) model with inspected migration to investigate the effect of border restriction as a public health measure to limit outbreak of coronavirus disease 2019. METHODS: We apply a modified metapopulation SEIR model with inspected migration with simulating population migration, and incorporating parameters such as efficiency of custom inspection in blocking infected travelers in the model. The population sizes were retrieved from government reports, while the number of COVID-19 patients were retrieved from Hong Kong Department of Health and China Centre for Disease Control (CDC) data. The R0 was obtained from previous clinical studies. RESULTS: Complete border closure can help to reduce the cumulative COVID-19 case number and mortality in Hong Kong by 13.99% and 13.98% respectively. To prevent full occupancy of isolation facilities in Hong Kong; effective public health measures to reduce local R0 to below 1.6 was necessary, apart from having complete border closure. CONCLUSIONS: Early complete travel restriction is effective in reducing cumulative cases and mortality. However, additional anti-COVID-19 measures to reduce local R0 to below 1.6 are necessary to prevent COVID-19 cases from overwhelming hospital isolation facilities.

Composite likelihood inference for ordinal periodontal data with replicated spatial patterns.

Spatial ordinal data observed separately for multiple subjects are common in biomedical research, yet statistical methodology for such ordinal data analysis is limited. The existing methodology often assumes a single realization of spatial ordinal data without replications, a commonplace in disease mapping studies, and thus are not directly applicable. Motivated by a dataset evaluating periodontal disease (PD) status, we propose a multisubject spatial ordinal model that assumes a geostatistical spatial structure within a regression framework through an elegant latent variable representation. For achieving computational scalability within a classical inferential framework, we develop a maximum composite likelihood method for parameter estimation, and establish the asymptotic properties of the parameter estimates. Another major contribution is the development of model diagnostic measures for our dependent data scenario using generalized surrogate residuals. A simulation study suggests sound finite sample properties of the proposed methods. We also illustrate our proposed methodology via application to the motivating PD dataset. A companion R package clordr is available for easy implementation.

Rapid Hygiene Assay Sensitive to Cumulative Adenylate Homologues Exhibits Equal or Higher Frequencies of Soil Contamination Detection than Assay Limited to ATP Detection.

ABSTRACT: Based upon regulatory and food industry-driven food safety standards, there is a need for rapid, accurate methods for assessing sanitary conditions. A commonly used assay is based on the assessment of the biochemical molecule adenosine triphosphate (ATP). A more recent assay, the total adenylate homologue-based (AXP) assay, targets the cumulative presence of ATP and its dephosphorylated homologues, adenosine diphosphate and adenosine monophosphate. Yet there is little information that compares the practical performance of these two assays. This work examined these two assay types with a comparative study in a grade A dairy foods processing plant and a licensed and inspected meat processing facility. A total of 1,920 concomitant analyses were conducted with main variables of assay type, processing facility type, and hygiene zone category. Statistical process control methodology was used to calculate 95% confidence control limits; data beyond those limits were considered contamination events. Results demonstrated that overall, the AXP assay detected contamination events approximately two times more often than the assay based on ATP only. This increase in the rate of contamination event detection was especially prevalent in the meat processing facility, where across all hygienic zones, there were 38 versus 85 contaminations events detected for the ATP and AXP assays, respectively. Across hygiene zones, the AXP data displayed either an equal or an increased incidence of soil detection compared with data from the ATP assay. This study provides applied evidence that assays solely dependent on ATP concentrations are less able to detect soil contaminants under conditions that favor ATP dephosphorylation reactions.

Territory-Wide Study on Hospital Admissions for Asthma Exacerbations in the COVID-19 Pandemic.

Rationale: Patients with asthma were advised to avoid coronavirus disease (COVID-19) and comply with medication during the COVID-19 pandemic. Respiratory tract infection is a common cause of asthma exacerbations. There has not been evidence suggesting the link between COVID-19 and asthma exacerbation, especially in places with dramatic responses in infection control with universal masking and aggressive social distancing. Objectives: To assess the number for admissions of asthma exacerbations in January to April 2020 in Hong Kong with reference to admission in the past 5 years. Methods: Admission records of asthma exacerbations were retrieved from the Clinical Data Analysis and Reporting System. Patients aged 18 years or older with a known history of asthma admitted for asthma exacerbation were included. Log-linear was used to model count, with year and masking used as covariate and further analysis on ambient temperature and length of hospital stays. Fisher's exact test was used to compare the mortality rate and mechanical ventilation between the periods. Admissions for myocardial infarction, ischemic stroke, and gastric ulcer were included as controls. Results: The number of admissions for asthma exacerbations significantly decreased by 53.2% (95% confidence interval [CI], 50.4-55.8%) in 2020 compared with monthly average admission in 2015-2019, with a higher magnitude of decrease compared with control diagnoses. Admissions for asthma exacerbations decreased by 2.0% (95% CI, 1.8-2.2%) with every 1°C (1.8°F) increase in temperature and by 0.8% with every 1% increase in masking (95% CI, 0.8-0.9%). Conclusions: Hospitalization number for asthma exacerbations significantly decreased in early 2020, with similar length of stay. This was observed with concomitant practice of universal masking and social distancing during the COVID-19 pandemic in Hong Kong. We proposed that universal masking and social distancing reduced respiratory viral infection, leading to fewer hospital admissions for asthma exacerbations.

Significant reduction in hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in Hong Kong during coronavirus disease 2019 pandemic.

BACKGROUND: Chronic respiratory diseases are risk factors for severe disease in coronavirus disease 2019 (COVID-19). Respiratory tract infection is one of the commonest causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). There has not been evidence suggesting the link between COVID-19 and AECOPD, especially in places with dramatic responses in infection control with universal masking and aggressive social distancing. METHODS: This is a retrospective study to assess the number of admissions of AECOPD in the first three months of 2020 in Queen Mary Hospital with reference to the admissions in past five years. Log-linear model was used for statistical inference of covariates, including percentage of masking, air quality health index and air temperature. RESULTS: The number of admissions for AECOPD significantly decreased by 44.0% (95% CI 36.4%-52.8%, p < 0.001) in the first three months of 2020 compared with the monthly average admission in 2015-2019. Compare to same period of previous years, AECOPD decreased by 1.0% with each percent of increased masking (p < 0.001) and decreased by 3.0% with increase in 1 °C in temperature (p = 0.045). The numbers of admissions for control diagnoses (heart failure, intestinal obstruction and iron deficiency anaemia) in the same period in 2020 were not reduced. CONCLUSIONS: The number of admissions for AECOPD decreased in first three months of 2020, compared with previous years. This was observed with increased masking percentage and social distancing in Hong Kong. We postulated universal masking and social distancing during COVID-19 pandemics both contributed in preventing respiratory tract infections hence AECOPD.

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses.

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource-intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT-PCR results. Our final model had high predictive power and only included geographic location and rRT-PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north-central states and the south-eastern region of Alaska. Lower rRT-PCR Ct-values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct-value) using the rRT-PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.

A pairwise likelihood-based approach for changepoint detection in multivariate time series models.

This paper develops a composite likelihood-based approach for multiple changepoint estimation in multivariate time series. We derive a criterion based on pairwise likelihood and minimum description length for estimating the number and locations of changepoints and for performing model selection in each segment. The number and locations of the changepoints can be consistently estimated under mild conditions and the computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studies and real data examples demonstrate the statistical and computational efficiency of the proposed method.