Bull and bear markets during the COVID-19 pandemic.

The COVID-19 pandemic has caused severe disruption worldwide. We analyze the aggregate U.S. stock market during this period, including implications for both short and long-horizon investors. We identify bull and bear market regimes including their bull correction and bear rally components, demonstrate our model's performance in capturing periods of significant regime change, and provide weekly forecasts that improve risk management and investment decisions. An investment strategy that uses out-of-sample forecasts for market states outperforms a buy and hold strategy during the pandemic by a wide margin, both in terms of annualized returns and Sharpe ratios.

The influence of physical characteristics on the resting energy expenditure of youth: A meta-analysis.

OBJECTIVE: To examine the literature on resting energy expenditure (REE) of youth and determine the influence of age, sex, BMI, and body composition on REE. METHODS: A literature search was conducted using PubMed, BIOSIS Previews, NTIS, EMBASE, MEDLINE, and Pascal databases for studies with data on resting metabolic rate, REE, resting oxygen uptake (or VO2 ) in healthy children, youth, or adolescents (age = 1-18 years). Over 200 publications were identified; sixty-one publications met criteria and were included in the meta-analyses, resulting in 142 study population estimates (totaling 5,397 youth) of REE. RESULTS: Pooled mean was 1414 kcal·day-1 with a significant and moderate-to-high between-study heterogeneity [Q(140) = 7912.42, P < 0.001; I2 = 98.97%]. A significantly greater (P < 0.001) pooled mean kcal·day-1 was estimated for studies with male participants (1519 kcal·day-1 ) comparing to studies with female participants (1338 kcal·day-1 ). Age, height, and body mass resulted in the highest R2 of 86.4 for males and 83.9% for females. Fat free mass and body mass index (BMI) did not improve total R2 . CONCLUSIONS: These data suggest that using a linear equation including age, height, and body mass to estimate REE based on kcal·day-1 is more accurate than estimates based on body mass kcal·kg-1 ·h-1 . Further, if kcal·kg-1 ·h-1 is used, including a quadratic component for the physical characteristics improves the predictive ability of the equation. Regardless of the metric, separate equations should be used for each sex.

A Meta-Analysis of Sampled Maximal Aerobic Capacity Data for Boys Aged 11 Years Old or Less Obtained by Cycle Ergometry.

The aim of this study was to develop distributions of VO2max based on measured values that exist in the literature in prepubertal boys using cycle ergometry. PRISMA guidelines were followed in conducting this research. One database was searched for peak and maximal VO2 values in healthy boys with mean age under 11 years old. Data were split into articles reporting absolute and relative VO2max values and analyzed accordingly. Multilevel models grounded in Bayesian principles were used. We investigated associations between VO2max and body mass, year of the study, and country of origin. Differences in "peak" and "maximal" VO2 were assessed. Absolute VO2max (Lmin-1) increases with age (P ~100%) but mean relative VO2max does not change (P ~100%). Absolute VO2max is higher in more recent studies (P = 95.7 ± 0.3%) and mean relative VO2max is lower (P = 99.6 ± 0.1%). Relative VO2max in the USA is lower compared with boys from other countries (P = 98.8 ± 0.2%), but there are no differences in absolute values. Mean aerobic capacity estimates presented as "peak" values are higher than "maximal" values on an absolute basis (P = 97.5 ± 0.3%) but not on a relative basis (P = 99.6 ± 0.1%). Heavier boys have lower cardiorespiratory fitness (P ≈ 100%), and body mass seems to be increasing faster with age in the USA compared with other countries (P = 92.3 ± 0.3%). New reference values for cardiorespiratory fitness are presented for prepubertal boys obtained with cycle ergometry. This is new, as no reference values have been determined so far based on actual measured values in prepubertal boys. Aerobic capacity normalized to body weight does not change with age. Cardiorespiratory fitness in prepubertal boys is declining, which is associated with increasing body mass over the last few decades. Lastly, this study did not find any statistically significant difference in the sample's mean aerobic capacity estimates using the "peak" and "maximum" distinctions identified in the literature.

Developing meaningful cohorts for human exposure models.

This paper summarizes numerous statistical analyses focused on the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD), used by many exposure modelers as the basis for data on what people do and where they spend their time. In doing so, modelers tend to divide the total population being analyzed into "cohorts", to reduce extraneous interindividual variability by focusing on people with common characteristics. Age and gender are typically used as the primary cohort-defining attributes, but more complex exposure models also use weather, day-of-the-week, and employment attributes for this purpose. We analyzed all of these attributes and others to determine if statistically significant differences exist among them to warrant their being used to define distinct cohort groups. We focused our attention mostly on the relationship between cohort attributes and the time spent outdoors, indoors, and in motor vehicles. Our results indicate that besides age and gender, other important attributes for defining cohorts are the physical activity level of individuals, weather factors such as daily maximum temperature in combination with months of the year, and combined weekday/weekend with employment status. Less important are precipitation and ethnic data. While statistically significant, the collective set of attributes does not explain a large amount of variance in outdoor, indoor, or in-vehicle locational decisions. Based on other research, parameters such as lifestyle and life stages that are absent from CHAD might have reduced the amount of unexplained variance. At this time, we recommend that exposure modelers use age and gender as "first-order" attributes to define cohorts followed by physical activity level, daily maximum temperature or other suitable weather parameters, and day type possibly beyond a simple weekday/weekend classification.

Using human activity data in exposure models: analysis of discriminating factors.

This paper tests factors thought to be important in explaining the choices people make in where they spend time. Three aggregate locations are analyzed: outdoors, indoors, and in-vehicles for two different sample groups: a year-long (longitudinal) sample of one individual and a cross-sectional sample of 169 individuals from the US Environmental Protection Agency's Consolidated Human Activity Database (CHAD). The cross-sectional sample consists of persons similar to the longitudinal subject in terms of age, work status, education, and residential type. The sample groups are remarkably similar in the time spent per day in the tested locations, although there are differences in participation rates: the percentage of days frequenting a particular location. Time spent outdoors exhibits the most relative variability of any location tested, with in-vehicle time being the next. The factors found to be most important in explaining daily time usage in both sample groups are: season of the year, season/temperature combinations, precipitation levels, and day-type (work/nonwork is the most distinct, but weekday/weekend is also significant). Season, season/temperature, and day-type are also important for explaining time spent indoors. None of the variables tested are consistent in explaining in-vehicle time in either the cross-sectional or longitudinal samples. Given these findings, we recommend that exposure modelers subdivide their population activity data into at least season/temperature, precipitation, and day-type "cohorts" as these factors are important discriminating variables affecting where people spend their time.

Understanding variability in time spent in selected locations for 7-12-year old children.

This paper summarizes a series of analyses of clustered, sequential activity/location data collected by Harvard University for 160 children aged 7-12 years in Southern California (Geyh et al., 2000). The main purpose of the paper is to understand intra- and inter-variability in the time spent by the sample in the outdoor location, the location exhibiting the most variability of the ones evaluated. The data were analyzed using distribution-free hypothesis-testing (K-S tests of the distributions), generalized linear modeling techniques, and random-sampling schemes that produced "cohorts" whose descriptive statistical characteristics were evaluated against the original dataset. Most importantly, our analyses indicate that subdividing the population into appropriate cohorts better replicates parameters of the original data, including the interclass correlation coefficient (ICC), which is a relative measure of the intra- and inter-individual variability inherent in the original data. While the findings of our analyses are consistent with previous assessments of "time budget" and physical activity data, they are constrained by the rather homogeneous sample available to us. Owing to a general lack of longitudinal human activity/location data available for other age/gender cohorts, we are unable to generalize our findings to other population subgroups.

Frequency of mouthing behavior in young children.

Young children may be more likely than adults to be exposed to pesticides following a residential application as a result of hand- and object-to-mouth contacts in contaminated areas. However, relatively few studies have specifically evaluated mouthing behavior in children less than 5 years of age. Previously unpublished data collected by the Fred Hutchinson Cancer Research Center (FHCRC) were analyzed to assess the mouthing behavior of 72 children (37 males/35 females). Total mouthing behavior data included the daily frequency of both mouth and tongue contacts with hands, other body parts, surfaces, natural objects, and toys. Eating events were excluded. Children ranged in age from 11 to 60 months. Observations for more than 1 day were available for 78% of the children. The total data set was disaggregated by gender into five age groups (10-20, 20-30, 30-40, 40-50, 50-60 months). Statistical analyses of the data were then undertaken to determine if significant differences existed among the age/gender subgroups in the sample. A mixed effects linear model was used to test the associations among age, gender, and mouthing frequencies. Subjects were treated as random and independent, and intrasubject variability was accounted for with an autocorrelation function. Results indicated that there was no association between mouthing frequency and gender. However, a clear relationship was observed between mouthing frequency and age. Using a tree analysis, two distinct groups could be identified: children < or = 24 and children >24 months of age. Children < or = 24 months exhibited the highest frequency of mouthing behavior with 81+/-7 events/h (mean+/-SE) (n=28 subjects, 69 observations). Children >24 months exhibited the lowest frequency of mouthing behavior with 42+/-4 events/h (n=44 subjects, 117 observations). These results suggest that children are less likely to place objects into their mouths as they age. These changes in mouthing behavior as a child ages should be accounted for when assessing aggregate exposure to pesticides in the residential environment.

Examining variations of resting metabolic rate of adults: a public health perspective.

PURPOSE: There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcal·kg(-1)·h(-1)) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcal·kg(-1)·h(-1)). METHODS: Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI). RESULTS: The mean value for RMR was 0.863 kcal·kg(-1)·h(-1) (95% CI = 0.852-0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI ≥ 30 kg·m(-2) had the lowest RMR (<0.741 kcal·kg(-1)·h(-1)). CONCLUSIONS: No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10% for men and almost 15% for women and be as high as 20%-30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.

Statistical properties of longitudinal time-activity data for use in human exposure modeling.

Understanding the longitudinal properties of the time spent in different locations and activities is important in characterizing human exposure to pollutants. The results of a four-season longitudinal time-activity diary study in eight working adults are presented, with the goal of improving the parameterization of human activity algorithms in EPA's exposure modeling efforts. Despite the longitudinal, multi-season nature of the study, participant non-compliance with the protocol over time did not play a major role in data collection. The diversity (D)--a ranked intraclass correlation coefficient (ICC)-- and lag-one autocorrelation (A) statistics of study participants are presented for time spent in outdoor, motor vehicle, residential, and other-indoor locations. Day-type (workday versus non-workday, and weekday versus weekend), season, temperature, and gender differences in the time spent in selected locations and activities are described, and D & A statistics are presented. The overall D and ICC values ranged from approximately 0.08-0.26, while the mean population rank A values ranged from approximately 0.19-0.36. These statistics indicate that intra-individual variability exceeds explained inter-individual variability, and low day-to-day correlations among locations. Most exposure models do not address these behavioral characteristics, and thus underestimate population exposure distributions and subsequent health risks associated with environmental exposures.

Investigating the American Time Use Survey from an exposure modeling perspective.

This paper describes an evaluation of the US Bureau of Labor Statistics' American Time Use Survey (ATUS) for potential use in modeling human exposures to environmental pollutants. The ATUS is a large, on-going, cross-sectional survey of where Americans spend time and what activities they undertake in those locations. The data are reported as a series of sequential activities over a 24-h time period--a "diary day"--starting at 0400 hours. Between 12,000 and 13,000 surveys are obtained each year and the Bureau has plans to continue ATUS for the foreseeable future. The ATUS already has about 73,000 diary days of data, more than twice as many as that which currently exists in the US Environmental Protection Agency's (EPA) "Consolidated Human Activity Database" (CHAD) that the Agency uses for exposure modeling purposes. There are limitations for using ATUS in modeling human exposures to environmental pollutants. The ATUS does not report the location for a number of activities regarded as "personal." For 2006, personal activities with missing location information totaled 572 min/day, on average, for survey participants: about 40% of their day. Another limitation is that ATUS does not distinguish between indoor and outdoor activities at home, two of the traditional locational demarcations used in human exposure modeling. This lack of information affects exposure estimates to both indoor and outdoor air pollutants and potentially affects non-dietary ingestion estimates for children, which can vary widely depending on whether or not a child is indoors. Finally, a detailed analysis of the work travel activity in a subsample from ATUS 2006 indicates that the coding scheme is not fully consistent with a CHAD-based exposure modeling approach. For ATUS respondents in this subsample who reported work as an activity, roughly 48% of their days were missing work travel at one or both ends of the work shift or reported within work-shift travel inconsistently. An extensive effort would be needed to recode work travel data from ATUS for EPA's exposure modeling purposes.

Analyses of school commuting data for exposure modeling purposes.

Human exposure models often make the simplifying assumption that school children attend school in the same census tract where they live. This paper analyzes that assumption and provides information on the temporal and spatial distributions associated with school commuting. The data were obtained using Oak Ridge National Laboratory's LandScan USA population distribution model applied to Philadelphia, PA. It is a high-resolution model used to allocate individual school-aged children to both a home and school location, and to devise a minimum-time home-to-school commuting path (called a trace) between the two locations. LandScan relies heavily on Geographic Information System (GIS) data. With respect to school children attending school in their home census tract, the vast majority does not in Philadelphia. Our analyses found that: (1) about 32% of the students walk across two or more census tracts going to school and 40% of them walk across four or more census blocks; and (2) 60% drive across four or more census tracts going to school and 50% drive across 10 or more census blocks. We also find that: (3) using a 5-min commuting time interval - as opposed to the modeled "trace" - results in misclassifying the "actual" path taken in 90% of the census blocks, 70% of the block groups, and 50% of the tracts; (4) a 1-min time interval is needed to reasonably resolve time spent in the various census unit designations; and (5) approximately 50% of both the homes and schools of Philadelphia school children are located within 160 m of highly traveled roads, and 64% of the schools are located within 200 m. These findings are very important when modeling school children's exposures, especially, when ascertaining the impacts of near-roadway concentrations on their total daily body burden. As many school children also travel along these streets and roadways to get to school, a majority of children in Philadelphia are in mobile source-dominated locations most of the day. We hypothesize that exposures of school children in Philadelphia to benzene and particulate matter will be much higher than if home and school locations and commuting paths at a 1-min time resolution are not explicitly modeled in an exposure assessment. Undertaking such an assessment will be the topic of a future paper.

Intra- and inter-individual variability in location data for two U.S. health-compromised elderly cohorts.

This study provides descriptive statistical data on daily time spent in three locations of exposure assessment interest for two panel studies of health-compromised elderly individuals >65-year-old having multiple days of human activity data. The panel studies include individuals living in Los Angeles (CA) and Baltimore (MD) in various housing types. Three general locations are evaluated: outdoors, in vehicles, and total indoors. Of particular interest is providing information regarding the within- and between-individual variability in the time use data for the three locations. The data are analyzed using non-parametric statistics and alternative statistical models. Within and between variability are evaluated using intraclass correlation coefficients (ICCs); daily "lag-one" autocorrelation coefficients are also provided for the two samples. There were significant gender differences for selected seasonal and/or day-of-the-week metrics for: (1) outdoor time in Los Angeles, but not in Baltimore, and (2) in-vehicle time in both areas. Elderly women spent more time in these locations than similarly aged men. The ICC statistic indicates that most of the variability in the time spent in the three locations is due to intraindividual variability rather than to inter-individual variability. The results indicate that US Environmental Protection Agency should consider gender, day-of-the-week, and time-of-day data in its exposure modeling of daily activities undertaken by the health-compromised elderly population.

Modeling energy expenditure and oxygen consumption in human exposure models: accounting for fatigue and EPOC.

Human exposure and dose models often require a quantification of oxygen consumption for a simulated individual. Oxygen consumption is dependent on the modeled individual's physical activity level as described in an activity diary. Activity level is quantified via standardized values of metabolic equivalents of work (METS) for the activity being performed and converted into activity-specific oxygen consumption estimates. However, oxygen consumption remains elevated after a moderate- or high-intensity activity is completed. This effect, which is termed excess post-exercise oxygen consumption (EPOC), requires upward adjustment of the METS estimates that follow high-energy expenditure events, to model subsequent increased ventilation and intake dose rates. In addition, since an individual's capacity for work decreases during extended activity, methods are also required to adjust downward those METS estimates that exceed physiologically realistic limits over time. A unified method for simultaneously performing these adjustments is developed. The method simulates a cumulative oxygen deficit for each individual and uses it to impose appropriate time-dependent reductions in the METS time series and additions for EPOC. The relationships between the oxygen deficit and METS limits are nonlinear and are derived from published data on work capacity and oxygen consumption. These modifications result in improved modeling of ventilation patterns, and should improve intake dose estimates associated with exposure to airborne environmental contaminants.

A new method of longitudinal diary assembly for human exposure modeling.

Human exposure time-series modeling requires longitudinal time-activity diaries to evaluate the sequence of concentrations encountered, and hence, pollutant exposure for the simulated individuals. However, most of the available data on human activities are from cross-sectional surveys that typically sample 1 day per person. A procedure is needed for combining cross-sectional activity data into multiple-day (longitudinal) sequences that can capture day-to-day variability in human exposures. Properly accounting for intra- and interindividual variability in these sequences can have a significant effect on exposure estimates and on the resulting health risk assessments. This paper describes a new method of developing such longitudinal sequences, based on ranking 1-day activity diaries with respect to a user-chosen key variable. Two statistics, "D" and "A", are targeted. The D statistic reflects the relative importance of within- and between-person variance with respect to the key variable. The A statistic quantifies the day-to-day (lag-one) autocorrelation. The user selects appropriate target values for both D and A. The new method then stochastically assembles longitudinal diaries that collectively meet these targets. On the basis of numerous simulations, the D and A targets are closely attained for exposure analysis periods >30 days in duration, and reasonably well for shorter simulation periods. Longitudinal diary data from a field study suggest that D and A are stable over time, and perhaps over cohorts as well. The new method can be used with any cohort definitions and diary pool assignments, making it easily adaptable to most exposure models. Implementation of the new method in its basic form is described, and various extensions beyond the basic form are discussed.

Biologically based modeling of multimedia, multipathway, multiroute population exposures to arsenic.

This article presents an integrated, biologically based, source-to-dose assessment framework for modeling multimedia/multipathway/multiroute exposures to arsenic. Case studies demonstrating this framework are presented for three US counties (Hunderton County, NJ; Pima County, AZ; and Franklin County, OH), representing substantially different conditions of exposure. The approach taken utilizes the Modeling ENvironment for TOtal Risk studies (MENTOR) in an implementation that incorporates and extends the approach pioneered by Stochastic Human Exposure and Dose Simulation (SHEDS), in conjunction with a number of available databases, including NATA, NHEXAS, CSFII, and CHAD, and extends modeling techniques that have been developed in recent years. Model results indicate that, in most cases, the food intake pathway is the dominant contributor to total exposure and dose to arsenic. Model predictions are evaluated qualitatively by comparing distributions of predicted total arsenic amounts in urine with those derived using biomarker measurements from the NHEXAS--Region V study: the population distributions of urinary total arsenic levels calculated through MENTOR and from the NHEXAS measurements are in general qualitative agreement. Observed differences are due to various factors, such as interindividual variation in arsenic metabolism in humans, that are not fully accounted for in the current model implementation but can be incorporated in the future, in the open framework of MENTOR. The present study demonstrates that integrated source-to-dose modeling for arsenic can not only provide estimates of the relative contributions of multipathway exposure routes to the total exposure estimates, but can also estimate internal target tissue doses for speciated organic and inorganic arsenic, which can eventually be used to improve evaluation of health risks associated with exposures to arsenic from multiple sources, routes, and pathways.