Negative Binomial Mixed Models Research Articles

Independent associations of short- and long-term air pollution exposure with COVID-19 mortality among Californians.

The growing literature demonstrating air pollution associations on COVID-19 mortality contains studies predominantly examining long-term exposure, with few on short-term exposure, and rarely both together to estimate independent associations. Because mechanisms by which air pollution may impact COVID-19 mortality risk function over timescales ranging from years to days, and given correlation among exposure time windows, consideration of both short- and long-term exposure is of importance. We assessed the independent associations between COVID-19 mortality rates with short- and long-term air pollution exposure by modeling both concurrently. Using California death certificate data COVID-19-related deaths were identified, and decedent residential information used to assess short- (4-week mean) and long-term (6-year mean) exposure to particulate matter <2.5µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3). Negative binomial mixed models were fitted on weekly census tract COVID-19 mortality adjusting for potential confounders with random effects for county and census tract and an offset for population. Data were evaluated separately for two time periods March 16, 2020–October 18, 2020 and October 19, 2020–April 25, 2021, representing the Spring/Summer surges and Winter surge. Independent positive associations with COVID-19 mortality were observed for short- and long-term PM2.5 in both study periods, with strongest associations observed in the first study period: COVID-19 mortality rate ratio for a 2-μg/m3 increase in long-term PM2.5 was 1.13 (95%CI:1.09,1.17) and for a 4.7-μg/m3 increase in short-term PM2.5 was 1.05 (95%CI:1.02,1.08). Statistically significant positive associations were seen for both short- and long-term NO2 in study period 1, but short-term NO2 was not statistically significant in study period 2. Results for long-term O3 indicate positive associations, however, only marginal significance is achieved in study period 1. These findings support an adverse effect of long-term PM2.5 and NO2 exposure on COVID-19 mortality risk, independent of short-term exposure, and a possible independent effect of short-term PM2.5.

Trajectories of MMSE and MoCA scores across the healthy adult lifespan in the Italian population

BackgroundThis study compares the performance at the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) across the healthy adult lifespan in an Italian population sample.MethodsThe MMSE and MoCA were administered to 407 Italian healthy native-speakers (165 males; age range 20–93 years; education range 4–25 years). A generalized Negative Binomial mixed model was run to profile MMSE and MoCA scores across 8 different age classes (≤ 30; 31–40; 41–50; 51–60; 61–70; 71–80; 81–85; ≥ 86) net of education and sex.ResultsMMSE and MoCA total scores declined with age (p < 0.001), with the MoCA proving to be “more difficult” than the MMSE (p < 0.001). The Age*Test interaction (p < 0.001) indicates that the MoCA proved to profile a sufficiently linear involutional trend in cognition with advancing age and to be able to detect poorer cognitive performances in individuals aged ≥ 71 years. By contrast, MMSE scores failed in capturing the expected age-related trajectory, reaching a plateau in the aforementioned age classes.DiscussionThe MoCA seems to be more sensitive than the MMSE in detecting age-related physiological decline of cognitive functioning across the healthy adult lifespan. The MoCA might be therefore more useful than the MMSE as a test for general cognitive screening aims.

Associations between nighttime light and COVID-19 incidence and mortality in the United States.

COVID-19 has caused almost 770,000 deaths in the United States by November 2021. The nighttime light (NTL), representing the intensity of human activities, may reflect the degree of human contacts and therefore the intensity of COVID-19 transmission. This study intended to assess the associations between NTL differences and COVID-19 incidence and mortality among U.S. counties. The COVID-19 data of U.S. counties as of 31 December 2020 were collected. The average NTL values for each county in 2019 and 2020 were derived from satellite data. A negative binomial mixed model was adopted to assess the relationships between NTL intensity and COVID-19 incidence and mortality. Compared to the counties with the lowest NTL level (0.14–0.37 nW/cm2/sr), those with the highest NTL level (1.78–59.61 nW/cm2/sr) were related with 15% higher mortality rates (mortality rate ratio:1.15, 95 %CI: 1.02–1.30, p-value: 0.02) and 23% higher incidence rates (incidence rate ratio:1.23, 95 %CI: 1.13–1.34, p-value < 0.0001). Our study suggested that more intensive NTL was related with higher incidence and mortality rates of COVID-19, and NTL had a stronger correlation with the COVID-19 incidence rate than mortality rate. Our findings have contributed solid epidemiological evidence to the existing COVID-19 knowledge pool, and would help policymakers develop interventions when faced with the potential risk of the following outbreaks.

Social mixing patterns relevant to infectious diseases spread by close contact in urban Blantyre, Malawi

IntroductionUnderstanding human mixing patterns relevant to infectious diseases spread through close contact is vital for modelling transmission dynamics and optimisation of disease control strategies. Mixing patterns in low-income countries like Malawi are not well known. MethodologyWe conducted a social mixing survey in urban Blantyre, Malawi between April and July 2021 (between the 2nd and 3rd wave of COVID-19 infections). Participants living in densely-populated neighbourhoods were randomly sampled and, if they consented, reported their physical and non-physical contacts within and outside homes lasting at least 5 min during the previous day. Age-specific mixing rates were calculated, and a negative binomial mixed effects model was used to estimate determinants of contact behaviour. ResultsOf 1201 individuals enroled, 702 (58.5%) were female, the median age was 15 years (interquartile range [IQR] 5–32) and 127 (10.6%) were HIV-positive. On average, participants reported 10.3 contacts per day (range: 1–25). Mixing patterns were highly age-assortative, particularly those within the community and with skin-to-skin contact. Adults aged 20–49 y reported the most contacts (median:11, IQR: 8–15) of all age groups; 38% (95%CI: 16–63) more than infants (median: 8, IQR: 5–10), who had the least contacts. Household contact frequency increased by 3% (95%CI: 2–5) per additional household member. Unemployed participants had 15% (95%CI: 9–21) fewer contacts than other adults. Among long range (>30 m away from home) contacts, secondary school children had the largest median contact distance from home (257 m, IQR 78–761). HIV-positive status in adults >=18 years-old was not associated with changed contact patterns (rate ratio: 1.01, 95%CI: (0.91–1.12)). During this period of relatively low COVID-19 incidence in Malawi, 301 (25.1%) individuals stated that they had limited their contact with others due to COVID-19 precautions; however, their reported contacts were 8% (95%CI: 1–13) higher. ConclusionIn urban Malawi, contact rates, are high and age-assortative, with little reported behavioural change due to either HIV-status or COVID-19 circulation. This highlights the limits of contact-restriction-based mitigation strategies in such settings and the need for pandemic preparedness to better understand how contact reductions can be enabled and motivated.

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

The association between greenness exposure and COVID-19 incidence in South Korea: An ecological study

BackgroundThe rapid spread of COVID-19 has caused an emergency situation worldwide. Investigating the association between environmental characteristics and COVID-19 incidence can be of the occurrence and transmission. The objective of this study was to evaluate the association between greenness exposure and COVID-19 cases at the district levels in South Korea. We also explored this association by considering several environmental indicators. MethodsDistrict-level data from across South Korea were used to model the cumulative count of COVID-19 cases per 100,000 persons between January 20, 2020, and February 25, 2021. Greenness exposure data were derived from the Environmental Geographic Information Service of the Korean Ministry of Environment. A negative binomial mixed model evaluated the association between greenness exposure and COVID-19 incidence rate at the district level. Furthermore, we assessed this association between demographic, socioeconomic, environmental statuses, and COVID-19 incidence. ResultsData from 239 of 250 districts (95.6%) were included in the analyses, resulting in 127.89 COVID-19 cases per 100,000 persons between January 20, 2020 and February 25, 2021. Several demographic and socioeconomic variables, districts with a higher rate of natural greenness exposure, were significantly associated with lower COVID-19 incidence rates (incidence rate ratio (IRR), 0.70; 95% confidence interval (CI), 0.54–0.90; P-value = 0.008) after adjusting covariates, but no evidence for the association between built greenness and COVID-19 incidence rates was found. ConclusionIn this ecological study of South Korea, we found that higher rates of exposure to natural greenness were associated with lower rates of COVID-19 cases.

Personal Characteristics Associated with Ecological Momentary Assessment Compliance in Adult Cochlear Implant Candidates and Users.

Ecological momentary assessment (EMA) often places high physical and mental burden on research participants compared with retrospective self-reports. The high burden could result in noncompliance with the EMA sampling scheme protocol. It has been a concern that certain types of participants could be more likely to have low compliance, such as those who have severe hearing loss and poor speech recognition performance, are employed, are not familiar with technologies used to implement EMA (e.g., smartphones), and have poorer cognitive abilities. Noncompliance dependent on personal characteristics could negatively impact the generalizability of EMA research. This article aims to determine personal characteristics associated with EMA compliance in a group of adult cochlear implant (CI) candidates and users. An observational study. Fifty-eight adults who were either scheduled to received CIs or were experienced CI users completed the study. Participants conducted smartphone-based EMA designed to assess an individual's daily auditory ecology for 1 week. EMA compliance was quantified using two metrics: the number of completed surveys and the response rate to the notification delivered by the EMA app. Personal characteristics (i.e., predictors) included age, gender, CI status (candidate or user), employment status (employed or not employed), smartphone ownership, speech recognition performance, social network size, level of depressive symptoms, and neurocognitive abilities. A word recognition test, questionnaires, and a test battery of neurocognitive assessments were used to measure the predictors. We used negative binomial regression and logistic mixed models to determine the factors associated with the number of completed surveys and the response rate, respectively. We hypothesized that, for example, employed participants with poorer speech recognition performance would have lower compliance. Contrary to the hypothesis, word recognition score was negatively associated with the number of completed surveys (p = 0.022). Holding all other variables constant, a 10-point (i.e., 10%) word recognition score decrease was associated with an 11% increase in the number of completed surveys. For the response rate, employment status was the only significant predictor (p < 0.0001). Consistent with our hypothesis, the odds of responding to EMA notifications for those who are not employed are 82% higher than the odds for those who are employed. No other studied personal characteristic was associated with compliance. For CI candidates and users, EMA compliance could be affected by personal characteristics such as speech recognition performance and employment status. Because (1) participants with poorer speech recognition performance do not necessarily have lower compliance and (2) most personal characteristics investigated in the present study (e.g., age, gender, smartphone ownership, and neurocognitive abilities) do not predict compliance, a wide range of participants could successfully conduct smartphone-based EMA.

Muscular tension as an indicator of acute stress in horses.

Horses’ muscular tension during acute stress remains unexplored. Our aim was to assess muscular, behavioral, cortisol, and hematocrit responses to social isolation (ISO), novel object exposure (NOV), and sham clipping (CLIP). Altered stress responses were expected. Eight mature Standardbred horses (four mares and four geldings) were exposed to acute stressors and a control period (CON) in a balanced, replicated 4×4 Latin Square experimental design with 3 min treatment periods and 10 min washout periods. Surface electromyography collected from the masseter, brachiocephalas, cervical trapezius, and longissimus dorsi was processed to derive average rectified value (ARV) and median frequency (MF) during the initial, middle, and final 30 s of treatments. ARV and MF data were log transformed then analyzed using a mixed model, repeated measures ANOVA along with plasma cortisol and hematocrit. Behavior data were analyzed using a negative binomial distribution mixed model ANOVA. CLIP resulted in greater (p < 0.05) log ARV in the masseter (1.5 + 1.5%, mean + SD) and brachiocepahlas (2.2 + 2.0%) than CON (−1.2 + 1.4%, 0.1 + 1.5%). ISO resulted in greater (p < 0.05) log ARV in the masseter (0.2 + 1.3%) and cervical trapezius (0.6 + 1.3%) than CON (−1.2 + 1.4%, −1.0 + 1.7%). ISO increased (p < 0.05) the total number of stress‐related behaviors and hematocrit. No changes in cortisol were observed. We suggest that muscular tension can be used as an indicator of acute stress in horses. Incorporating muscle activity into an array of measurements may provide a more nuanced understanding of stress responses.

Caregiver Language Input Supports Sentence Diversity in Young Children With Autism Spectrum Disorder.

Sentence diversity is a measure of early language development that has yet to be applied to individuals with autism spectrum disorder (ASD). The primary aim of this study was to identify whether children with ASD show change in sentence diversity over 6 months of treatment with Naturalistic Developmental Behavioral Intervention (NDBI). The secondary aim was to examine possible predictors of changes in children's sentence diversity, including caregiver use of NDBI strategies, naturally occurring instances of caregiver Toy Talk, and child characteristics. Fifty children with ASD (ages 2-4 years) and their caregivers, who were receiving NDBI, engaged in two 10-min video-recorded play interactions, 6 months apart. Child speech was transcribed and coded for sentence diversity. Caregiver input was transcribed and coded for naturally occurring Toy Talk. Zero-inflated negative binomial mixed models were used to explore predictors of change in child sentence diversity. Children's sentence diversity improved over time. Changes in caregiver NDBI strategy use and caregiver baseline Toy Talk were significant predictors of changes in sentence diversity, as were baseline age, nonverbal ratio IQ, and child sex. Additionally, a significant interaction of caregiver baseline Toy Talk and change in caregiver NDBI strategies emerged; the effect of caregiver baseline Toy Talk on children's sentence diversity change was stronger when NDBI strategy use improved. Sentence diversity is a developmentally sensitive measure of language development in ASD. NDBI strategies that facilitate reciprocal social communication, combined with input composed of declarative sentences with noun or third-person pronoun subjects, may provide optimal support for children's sentence development.

Zero-Inflated Negative Binomial Mixed Models for Predicting Number of Wildfires

Zero-Inflated Negative Binomial Mixed Models for Predicting Number of Wildfires

171. The Impact of COVID-19 on Healthcare-Associated Infections

BackgroundThe profound changes wrought by COVID-19 on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). ObjectiveEvaluate the association between COVID-19 surges and HAI or cluster ratesMethods Design: Prospective cohort studySetting148 HCA Healthcare-affiliated hospitals, 3/1/2020-9/30/2020, and a subset of hospitals with microbiology and cluster data through 12/31/2020PatientsAll inpatientsMeasurementsWe evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month at each hospital.ResultsCentral line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased (P ≤ 0.001 for all), with 60% (95% CI, 23 to 108%) more CLABSI, 43% (95% CI, 8 to 90%) more CAUTI, and 44% (95% CI, 10 to 88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection (CDI) was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus and Gram-negative organisms were each significantly associated with COVID-19 surges (P < 0.05 for all). Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased (P = 0.02).LimitationsVariations in surveillance and reporting may affect HAI data.Table 1. Effect of an increase in number of COVID-19 discharges on HAIs and hospital-onset pathogens Figure 1. Predicted mean HAI rates as COVID-19 discharges increase Predicted mean HAI rate by increasing monthly COVID-19 discharges. Panel a. CLABSI, Panel b, CAUTI Panel c. MRSA Bacteremia, Panel d. CDI. Data are stratified by small, medium and large hospitals.Figure 2. Monthly comparison of COVID discharges to clusters COVID-19 discharges and the number of clusters of hospital-onset pathogens are correlated throughout the pandemic.ConclusionCOVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention. Disclosures Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Eunice J. Blanchard, MSN RN, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Deborah S. Yokoe, MD, MPH, Nothing to disclose Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)

Timing of first antenatal care visits and number of items of antenatal care contents received and associated factors in Ethiopia: multilevel mixed effects analysis

BackgroundReceiving quality antenatal care (ANC) from skilled providers is essential to ensure the critical health circumstances of a pregnant woman and her child. Thus, this study attempted to assess which risk factors are significantly associated with the timing of antenatal care and the number of items of antenatal care content received from skilled providers in recent pregnancies among mothers in Ethiopia.MethodsThe data was extracted from the Ethiopian Demographic and Health Survey 2016. A total of 6645 mothers were included in the analysis. Multilevel mixed-effects logistic regression analysis and multilevel mixed Negative binomial models were fitted to find the factors associated with the timing and items of the content of ANC services. The 95% Confidence Interval of Odds Ratio/Incidence Rate Ratio, excluding one, was reported as significant.ResultsAbout 20% of the mothers initiated ANC within the first trimester, and only 53% received at least four items of antenatal care content. Being rural residents (IRR = 0.82; 95%CI: 0.75–0.90), wanting no more children (IRR = 0.87; 95%CI: 0.79–0.96), and the husband being the sole decision maker of health care (IRR = 0.88; 95%CI: 0.81–0.96), were associated with reduced items of ANC content received. Further, birth order of six or more (IRR = 0.74; 95%CI: 0.56–0.96), rural residence (IRR = 0.0.41; 95%CI: 0.34–0.51), and wanting no more children (IRR = 0.61; 95%CI: 0.48–0.77) were associated with delayed antenatal care utilization.ConclusionsRural residences, the poorest household wealth status, no education level of mothers or partners, unexposed to mass media, unwanted pregnancy, mothers without decision-making power, and considerable distance to the nearest health facility have a significant impact on delaying the timing of ANC visits and reducing the number of items of ANC received in Ethiopia. Mothers should start an antenatal care visit early to ensure that a mother receives all of the necessary components of ANC treatment during her pregnancy.

The gut microbiome of extremely preterm infants randomized to the early progression of enteral feeding

BackgroundEarly progression of feeding could influence the development of the gut microbiome.MethodsWe collected fecal samples from extremely preterm infants randomized to receive either early (feeding day 2) or delayed (feeding day 5) feeding progression. After study completion, we compared samples obtained at three different time points (week 1, week 2, and week 3) to determine longitudinal differences in specific taxa between the study groups using unadjusted and adjusted negative binomial and zero-inflated mixed models. Analyses were adjusted for a mode of delivery, breastmilk intake, and exposure to antibiotics.ResultsWe analyzed 137 fecal samples from 51 infants. In unadjusted and adjusted analyses, we did not observe an early transition to higher microbial diversity within samples (i.e., alpha diversity) or significant differences in microbial diversity between samples (i.e., beta diversity) in the early feeding group. Our longitudinal, single-taxon analysis found consistent differences in the genera Lactococcus, Veillonella, and Bilophila between groups.ConclusionsDifferences in single-taxon analyses independent of the mode of delivery, exposure to antibiotics, and breastmilk feeding suggest potential benefits of early progression of enteral feeding volumes. However, this dietary intervention does not appear to increase the diversity of the gut microbiome in the first 28 days after birth.Trial RegistrationClinicalTrials.gov identifier: NCT02915549.ImpactEarly progression of enteral feeding volumes with human milk reduces the duration of parenteral nutrition and the need for central venous access among extremely preterm infants.Early progression of enteral feeding leads to single-taxon differences in longitudinal analyses of the gut microbiome, but it does not appear to increase the diversity of the gut microbiome in the first 28 days after birth.Randomization in enteral feeding trials creates appealing opportunities to evaluate the effects of human milk diets on the gut microbiome.

Long-term air pollution and COVID-19 mortality rates in California: Findings from the Spring/Summer and Winter surges of COVID-19

A growing number of studies report associations between air pollution and COVID-19 mortality. Most were ecological studies at the county or regional level which disregard important local variability and relied on data from only the first few months of the pandemic. Using COVID-19 deaths identified from death certificates in California, we evaluated whether long-term ambient air pollution was related to weekly COVID-19 mortality at the census tract-level during the first ∼12 months of the pandemic. Weekly COVID-19 mortality for each census tract was calculated based on geocoded death certificate data. Annual average concentrations of ambient particulate matter <2.5 μm (PM2.5) and <10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) over 2014–2019 were assessed for all census tracts using inverse distance-squared weighting based on data from the ambient air quality monitoring system. Negative binomial mixed models related weekly census tract COVID-19 mortality counts to a natural cubic spline for calendar week. We included adjustments for potential confounders (census tract demographic and socioeconomic factors), random effects for census tract and county, and an offset for census tract population. Data were analyzed as two study periods: Spring/Summer (March 16-October 18, 2020) and Winter (October 19, 2020–March 7, 2021). Mean (standard deviation) concentrations were 10.3 (2.1) μg/m3 for PM2.5, 25.5 (7.1) μg/m3 for PM10, 11.3 (4.0) ppb for NO2, and 42.8 (6.9) ppb for O3. For Spring/Summer, adjusted rate ratios per standard deviation increase were 1.13 (95% confidence interval: 1.09, 1.17) for PM2.5, 1.16 (1.11, 1.21) for PM10, 1.06 (1.02, 1.10) for NO2, and 1.09 (1.04, 1.14) for O3. Associations were replicated in Winter, although they were attenuated for PM2.5 and PM10. Study findings support a relation between long-term ambient air pollution exposure and COVID-19 mortality. Communities with historically high pollution levels might be at higher risk of COVID-19 mortality.

Quantifying the Effects of Climate Factors on Carbon Monoxide Poisoning: A Retrospective Study in Taiwan.

Background: Carbon monoxide (CO) poisoning is the leading cause of poisoning death worldwide, but associations between CO poisoning and weather remain unclear.Objective: To quantify the influence of climate parameters (e.g., temperature, relative humidity, and wind speed) on the incidence risk of acute CO poisoning in Taiwan.Methods: We used negative binomial mixed models (NBMMs) to evaluate the influence of weather parameters on the incidence risk of acute CO poisoning. Subgroup analyses were conducted, based on the seasonality and the intentionality of acute CO poisoning cases.Results: We identified a total of 622 patients (mean age: 32.9 years old; female: 51%) with acute CO poisoning in the study hospital. Carbon monoxide poisoning was associated with temperature (beta: −0.0973, rate ratio (RR): 0.9073, p < 0.0001) but not with relative humidity (beta: 0.1290, RR: 1.1377, p = 0.0513) or wind speed (beta: −0.4195, RR: 0.6574, p = 0.0806). In the subgroup analyses, temperature was associated with the incidence of intentional CO poisoning (beta: 0.1076, RR: 1.1136, p = 0.0333) in spring and unintentional CO poisoning (beta: −0.1865, RR: 0.8299, p = 0.0184) in winter.Conclusion: Changes in temperature affect the incidence risk for acute CO poisoning, but the impact varies with different seasons and intentionality in Taiwan. Our findings quantify the effects of climate factors and provide fundamental evidence for healthcare providers to develop preventative strategies to reduce acute CO poisoning events.

Long-term Ambient Air Pollution Associated with Weekly COVID-19 Mortality Counts in California Census Tracts

BACKGROUND AND AIM: A growing number of studies have reported associations between ambient air pollution and COVID-19 mortality. Most of these studies are ecological, relying on county or regional level aggregate data which disregard important local variability. Using COVID-19 deaths identified from death certificates in California, we evaluated whether ambient air pollution related to weekly COVID-19 mortality at the census tract-level. METHODS: Weekly COVID-19 mortality for each California census tract was calculated based on geocoded death certificate data obtained from the Department of Public Health Vital Records. Annual average concentrations of ambient nitrogen dioxide (NO2), ozone (O3), particulate matter 2.5µm (PM2.5) and 10µm (PM10) over 2014-2019 were assessed for all census tracts using inverse distance-squared weighting based on data from the ambient air quality monitoring system. Negative binomial mixed models related weekly COVID-19 census tract mortality counts to a natural cubic spline for calendar time (df=7), with adjustment for potential confounders (census tract demographic and socioeconomic factors, state region), random effects for census tract and county, and an offset for census tract population. Each air pollutant was evaluated separately. RESULTS:From March 16 through October 25, 2020, there were 16,397 COVID-19-related deaths in California. Mean (standard deviation [SD]) 6-year annual average pollution concentrations for NO2, O3, PM25, and PM10 were 12.2 ppb (4.5), 42.8 ppb (7.0), 10.4 μg/m3 (2.2), and 25.5 μg/m3 (7.1), respectively. Preliminary results indicate positive associations between long-term air pollution concentrations and weekly COVID-19 census tract mortality rates. COVID-19 mortality rate ratios were 1.11 (95%CI: 1.07-1.15) and 1.15 (95%CI: 1.10-1.21) per SD increase in long-term PM2.5 and PM10, respectively. CONCLUSIONS:Study findings support a relation between chronic ambient air pollution exposure and weekly COVID-19 mortality. We are exploring how pollution effects may have varied over the course of the pandemic, as well as by individual-level characteristics (age, sex). KEYWORDS: air pollution, COVID-19, mortality, particulate matter, ozone, nitrogen dioxide

The Impact of Coronavirus Disease 2019 (COVID-19) on Healthcare-Associated Infections.

BackgroundThe profound changes wrought by COVID-19 on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates.MethodsIn 148 HCA Healthcare-affiliated hospitals, 3/1/2020-9/30/2020, and a subset of hospitals with microbiology and cluster data through 12/31/2020, we evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month.ResultsCentral line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased. There were 60% (95% CI, 23-108%) more CLABSI, 43% (95% CI, 8-90%) more CAUTI, and 44% (95% CI, 10-88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus and Gram-negative organisms were each significantly associated with COVID-19 surges. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased.ConclusionCOVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

The increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer’s disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data.

County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

BackgroundCOVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. ObjectivesWe evaluated whether greenness was related to COVID-19 incidence and mortality in the US. MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April–May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders. ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. DiscussionExposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.

Using shrinkage strategies to estimate fixed effects in zero-inflated negative binomial mixed model

In this paper, we address the estimation of fixed effects parameters in the zero-inflated negative binomial mixed model based on shrinkage estimators, namely linear shrinkage, pretest, shrinkage pretest, shrinkage, and positive-shrinkage estimators when the random effects are considered as nuisance parameters. We compare the performance of the shrinkage estimators to unrestricted and restricted estimators when certain prior subspace information is available. The asymptotic distributional biases and risks of the proposed estimators are obtained. We also conduct a Monte Carlo simulation study to compare the performance of each estimator in the sense of simulated relative efficiency. The results of simulation study show that the proposed estimation strategies perform strongly better than the maximum likelihood method. Finally, proposed methodologies are applied to a real dataset to appraise their performances.