In this study we developed a genome-based method for detecting Staphylococcus aureus subtypes from metagenome shotgun sequence data. We used a binomial mixture model and the coverage counts at > 100,000 known S. aureus SNP (single nucleotide polymorphism) sites derived from prior comparative genomic analysis to estimate the proportion of 40 subtypes in metagenome samples. We were able to obtain > 87% sensitivity and > 94% specificity at 0.025X coverage for S. aureus. We found that 321 and 149 metagenome samples from the Human Microbiome Project and metaSUB analysis of the New York City subway, respectively, contained S. aureus at genome coverage > 0.025. In both projects, CC8 and CC30 were the most common S. aureus clonal complexes encountered. We found evidence that the subtype composition at different body sites of the same individual were more similar than random sampling and more limited evidence that certain body sites were enriched for particular subtypes. One surprising finding was the apparent high frequency of CC398, a lineage often associated with livestock, in samples from the tongue dorsum. Epidemiologic analysis of the HMP subject population suggested that high BMI (body mass index) and health insurance are possibly associated with S. aureus carriage but there was limited power to identify factors linked to carriage of even the most common subtype. In the NYC subway data, we found a small signal of geographic distance affecting subtype clustering but other unknown factors influence taxonomic distribution of the species around the city.
Background: Heatwaves are becoming more frequent and may acutely increase the risk of stillbirth, a rare and severe pregnancy outcome. Objectives: Examine the association between multiple heatwave metrics and stillbirth in six U.S. states. Methods: Data were collected from fetal death and birth records in California (1996–2017), Florida (1991–2017), Georgia (1994–2017), Kansas (1991–2017), New Jersey (1991–2015), and Oregon (1991–2017). Cases were matched to controls 1:4 based on maternal race/ethnicity, maternal education, and county, and exposure windows were aligned (gestational week prior to stillbirth). County-level temperature data were obtained from Daymet and linked to cases and controls by residential county and the exposure window. Five heatwave metrics (1 categorical, 3 dichotomous, 1 continuous) were created using different combinations of the duration and intensity of hot days (mean daily temperature exceeding the county-specific 97.5th percentile) during the exposure window, as well as a continuous measure of mean temperature during the exposure window modeled using natural splines to allow for nonlinear associations. State-specific odds ratios (ORs) and 95% confidence intervals (CI) were estimated using conditional logistic regression models. State-specific results were pooled using a fixed-effects meta-analysis. Results: In our data set of 140,428 stillbirths (553,928 live birth controls), three of the five heatwave metrics examined were not associated with stillbirth. However, four consecutive hot days during the previous week was associated with a 3% increase in stillbirth risk (CI: 1.01, 1.06), and a 1 °C average increase over the threshold was associated with a 10% increase in stillbirth risk (CI: 1.04, 1.17). In continuous temperature analyses, there was a slight increased risk of stillbirth associated with extremely hot temperatures (≥ 35 °C). Discussion: Most heat wave definitions examined were not associated with acute changes in stillbirth risk; however, the most extreme heatwave durations and temperatures were associated with a modest increase in stillbirth risk.
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BMC Med Res Methodol. 2021; 21: 87. Published online 2021 Apr 26. doi: 10.1186/s12874-021-01278-x
PMCID: PMC8077733PMID: 33902463
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta
Shan Jiang,1 Joshua L. Warren,2 Noah Scovronick,3 Shannon E. Moss,1 Lyndsey A. Darrow,4 Matthew J. Strickland,4 Andrew J. Newman,5 Yong Chen,6 Stefanie T. Ebelt,3 and Howard H. Changcorresponding author1
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Associated Data
Supplementary Materials
Additional file 1: Figure S1. Structure of logic regression tree of extreme heat exposures for selected warm-season ED visit outcomes in Atlanta, Georgia, 1993–2012. Table S1. Summary of alternative extreme temperature metrics and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012. Table S2. Summary of alternative extreme temperature metrics with consecutive lags and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012. Table S3. Summary of extreme heat metrics from truncated continuous versus continuous temperature metric and their short-term associations with warm-season emergency department visits in Atlanta, 1993 to 2012.
12874_2021_1278_MOESM1_ESM.docx (61K)
GUID: 896D464B-D573-4663-A671-9860084458AB
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Abstract
Background
Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations.
Methods
Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area.
Results
For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient.
Conclusion
Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.
Thyroid hormones are essential for proper neurodevelopment in early life. There is evidence that exposure to polybrominated diphenyl ethers (PBDEs) affects thyroid function, but previous studies have been inconsistent, and no studies among children have been conducted in the United States where PBDE levels are particularly high. Serum levels of seven PBDE congeners and thyroid hormones and other thyroid parameters were measured in 80 children aged 1-5 years from the southeastern United States between 2011 and 2012. Parents of the children completed questionnaires with details on demographics and behaviors. Multivariate linear regression models were used to estimate the associations between serum PBDE levels, expressed as quartiles and as log-transformed continuous variables, and markers of thyroid function. BDE-47, 99, 100 and 153 were detected in > 60% of samples, and were summed (∑PBDE). PBDE congeners and ∑PBDE were positively associated with thyroid-stimulating hormone (TSH). A log-unit increase in ∑PBDE was associated with a 22.1% increase in TSH (95% CI: 2.0%, 47.7%). Compared with children in the lowest quartile of ∑PBDE exposure, children in higher quartiles had greater TSH concentrations as modeled on the log-scale (second quartile: β=0.32, 95% confidence interval (CI): -0.09, 0.74; third quartile: β=0.44, 95% CI: 0.04, 0.85; and fourth quartile: β=0.49, 95% CI: 0.09, 0.89). There was also a tendency toward lower total T 4 and higher free T 3 with increasing PBDE exposure. Results suggest that exposure to PBDEs during childhood subclinically disrupts thyroid hormone function, with impacts in the direction of hypothyroidism.
Background
The effect of heatwaves on adverse birth outcomes is not well understood and may vary by how heatwaves are defined. The study aims to examine acute associations between various heatwave definitions and preterm and early-term birth.
Methods
Using national vital records from 50 metropolitan statistical areas (MSAs) between 1982 and 1988, singleton preterm (< 37 weeks) and early-term births (37–38 weeks) were matched (1:1) to controls who completed at least 37 weeks or 39 weeks of gestation, respectively. Matching variables were MSA, maternal race, and maternal education. Sixty heatwave definitions including binary indicators for exposure to sustained heat, number of high heat days, and measures of heat intensity (the average degrees over the threshold in the past 7 days) based on the 97.5th percentile of MSA-specific temperature metrics, or the 85th percentile of positive excessive heat factor (EHF) were created. Odds ratios (OR) for heatwave exposures in the week preceding birth (or corresponding gestational week for controls) were estimated using conditional logistic regression adjusting for maternal age, marital status, and seasonality. Effect modification by maternal education, age, race/ethnicity, child sex, and region was assessed.
Results
There were 615,329 preterm and 1,005,576 early-term case-control pairs in the analyses. For most definitions, exposure to heatwaves in the week before delivery was consistently associated with increased odds of early-term birth. Exposure to more high heat days and more degrees above the threshold yielded higher magnitude ORs. For exposure to 3 or more days over the 97.5th percentile of mean temperature in the past week compared to zero days, the OR was 1.027 for early-term birth (95%CI: 1.014, 1.039). Although we generally found null associations when assessing various heatwave definitions and preterm birth, ORs for both preterm and early-term birth were greater in magnitude among Hispanic and non-Hispanic black mothers.
Conclusion
Although associations varied across metrics and heatwave definitions, heatwaves were more consistently associated with early-term birth than with preterm birth. This study’s findings may have implications for prevention programs targeting vulnerable subgroups as climate change progresses.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12940-021-00733-y.
Background: Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error. Methods: We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution. Results: Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center. Conclusion: Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.
Rationale: Certain outdoor air pollutants cause asthma exacerbations in children. To advance understanding of these relationships, further characterization of the dose–response and pollutant lag effects are needed, as are investigations of pollutant species beyond the commonly measured criteria pollutants.
Objectives: Investigate short-term associations between ambient air pollutant concentrations and emergency department visits for pediatric asthma.
Methods: Daily counts of emergency department visits for asthma or wheeze among children aged 5 to 17 years were collected from 41 Metropolitan Atlanta hospitals during 1993–2004 (n = 91,386 visits). Ambient concentrations of gaseous pollutants and speciated particulate matter were available from stationary monitors during this time period. Rate ratios for the warm season (May to October) and cold season (November to April) were estimated using Poisson generalized linear models in the framework of a case-crossover analysis.
Measurements and Main Results: Both ozone and primary pollutants from traffic sources were associated with emergency department visits for asthma or wheeze; evidence for independent effects of ozone and primary pollutants from traffic sources were observed in multipollutant models. These associations tended to be of the highest magnitude for concentrations on the day of the emergency department visit and were present at relatively low ambient concentrations.
Conclusions: Even at relatively low ambient concentrations, ozone and primary pollutants from traffic sources independently contributed to the burden of emergency department visits for pediatric asthma.
Background: Characterizing multipollutant health effects is challenging. We use classification and regression trees to identify multipollutant joint effects associated with pediatric asthma exacerbations and compare these results with those from a multipollutant regression model with continuous joint effects. Methods: We investigate the joint effects of ozone, NO<inf>2</inf> and PM<inf>2.5</inf> on emergency department visits for pediatric asthma in Atlanta (1999-2009), Dallas (2006-2009) and St. Louis (2001-2007). Daily concentrations of each pollutant were categorized into four levels, resulting in 64 different combinations or "Day-Types" that can occur. Days when all pollutants were in the lowest level were withheld as the reference group. Separate regression trees were grown for each city, with partitioning based on Day-Type in a model with control for confounding. Day-Types that appeared together in the same terminal node in all three trees were considered to be mixtures of potential interest and were included as indicator variables in a three-city Poisson generalized linear model with confounding control and rate ratios calculated relative to the reference group. For comparison, we estimated analogous joint effects from a multipollutant Poisson model that included terms for each pollutant, with concentrations modeled continuously. Results and discussion: No single mixture emerged as the most harmful. Instead, the rate ratios for the mixtures suggest that all three pollutants drive the health association, and that the rate plateaus in the mixtures with the highest concentrations. In contrast, the results from the comparison model are dominated by an association with ozone and suggest that the rate increases with concentration. Conclusion: The use of classification and regression trees to identify joint effects may lead to different conclusions than multipollutant models with continuous joint effects and may serve as a complementary approach for understanding health effects of multipollutant mixtures.
BACKGROUND: In 1973–1974, Michigan residents were exposed to polybrominated biphenyls (PBBs) through an accidental contamination of the food supply. Residents were enrolled in a registry assembled after the incident, and they and their children participated in follow-up studies to assess subsequent health outcomes. OBJECTIVES: We evaluated associations between serum PBBs and polychlorinated biphenyls (PCBs) and markers of thyroid function among Michigan adults. METHODS: Serum concentrations of four PBB and four PCB congeners were measured at least once in 753 adults, including 79 women who participated in a 2004–2006 study and 683 women and men with follow-up during 2012–2015. Participants completed questionnaires on health conditions (including physician-diagnosed thyroid disease), behaviors, and demographics. Thyroid hormones were measured in a subset without thyroid disease (n = 551). In multivariable linear regression models, PBB and PCB congener concentrations, on both the volume (nanogram/milliliter) and lipid (na-nogram/gram lipid) basis, were assessed in relation to thyroid hormones. Logistic regression models were used to estimate associations between serum PBBs and PCBs and thyroid disease. RESULTS: Thyroid disease was common (18% overall; 25% among women). Among women, all odds ratios (ORs) for PBB-153 and thyroid disease were positive for quintiles above the reference level, but estimates were imprecise and were without a monotonic increase. For an interquartile range (IQR) increase in PBB-153 (0:43 ng/mL), the OR (any thyroid disease) =1.12; (95% CI: 0.83, 1.52) (n = 105 cases); for hypothyroidism, OR = 1.35 (95% CI: 0.86, 2.13) (n = 49 cases). There were 21 cases of thyroid disease in men [OR = 0.69 (95% CI: 0.33); 1.44 for an IQR increase (0:75 ng/mL) in serum PBB-153]. PCB congeners were statistically significantly associated with greater total and free thyroxine and total triiodothyronine among women and with total and free triiodothyronine among men in lipid-standardized models. CONCLUSIONS: We found some evidence to support associations of PBBs and PCBs with thyroid disease and thyroid hormone levels.
Prenatal air pollution exposure is frequently estimated using maternal residential location at the time of delivery as a proxy for residence during pregnancy. We describe residential mobility during pregnancy among 19,951 children from the Kaiser Air Pollution and Pediatric Asthma Study, quantify measurement error in spatially resolved estimates of prenatal exposure to mobile source fine particulate matter (PM 2.5) due to ignoring this mobility, and simulate the impact of this error on estimates of epidemiologic associations. Two exposure estimates were compared, one calculated using complete residential histories during pregnancy (weighted average based on time spent at each address) and the second calculated using only residence at birth. Estimates were computed using annual averages of primary PM 2.5 from traffic emissions modeled using a Research LINE-source dispersion model for near-surface releases (RLINE) at 250 m resolution. In this cohort, 18.6% of children were born to mothers who moved at least once during pregnancy. Mobile source PM 2.5 exposure estimates calculated using complete residential histories during pregnancy and only residence at birth were highly correlated (r S > 0.9). Simulations indicated that ignoring residential mobility resulted in modest bias of epidemiologic associations toward the null, but varied by maternal characteristics and prenatal exposure windows of interest (ranging from â '2% to â '10% bias).