The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths.Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1).The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2)This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.     

Relating Weather Types to Asthma-Related Hospital Admissions in New York State

Many previous studies have looked into the relationship between asthma and individual weather variables, but comparatively few have looked at this relationship using holistic weather types (WTs). Utilizing the Spatial Synoptic Classification, this research considers up to 6 days of lag time while investigating the asthma-to-WT relationship in two age groups (under 18 and 18 and over) throughout New York State. Results indicate that a cold and dry WT in autumn corresponds to increased asthma admissions and spike days in admissions in New York City (NYC) for the school-aged population, while hot and dry WTs in summer correspond to spike days in asthma admissions in both age groups. However, results vary considerably for other regions, seasons and WTs, and spike day analysis yields clearer results than the analysis of total anomalous admissions. When stratified by multiple regions and age groups, the sample size of daily asthma admissions is a limiting factor outside of NYC.     

Association of allergic rhinitis or asthma with pollen and chemical pollutants in Szeged,Hungary, 1999–2007

The effect of biological (pollen) and chemical air pollutants on respiratory hospital admissions for the Szeged region in Southern Hungary is analysed. A 9-year (1999–2007) database includes—besides daily number of respiratory hospital admissions—daily mean concentrations of CO, PM₁₀, NO, NO₂, O₃ and SO₂. Two pollen variables (Ambrosia and total pollen excluding Ambrosia) are also included. The analysis was performed for patients with chronic respiratory complaints (allergic rhinitis or asthma bronchiale) for two age categories (adults and the elderly) of males and females. Factor analysis was performed to clarify the relative importance of the pollutant variables affecting respiratory complaints. Using selected low and high quantiles corresponding to probability distributions of respiratory hospital admissions, averages of two data sets of each air pollutant variable were evaluated. Elements of these data sets were chosen according to whether actual daily patient numbers were below or above their quantile value. A nonparametric regression technique was applied to discriminate between extreme and non-extreme numbers of respiratory admissions using pollen and chemical pollutants as explanatory variables. The strongest correlations between extreme patient numbers and pollutants can be observed during the pollen season of Ambrosia, while the pollen-free period exhibits the weakest relationships. The elderly group with asthma bronchiale is characterised by lower correlations between extreme patient numbers and pollutants compared to adults and allergic rhinitis, respectively. The ratio of the number of correct decisions on the exceedance of a quantile resulted in similar conclusions as those obtained by using multiple correlations.     

The impact of heat,cold, and heat waves on hospital admissions in eight cities in Korea

Although the impact of temperature on mortality is well documented, relatively fewer studies have evaluated the associations of temperature with morbidity outcomes such as hospital admissions, and most studies were conducted in North America or Europe. We evaluated weather and hospital admissions including specific causes (allergic disease, asthma, selected respiratory disease, and cardiovascular disease) in eight major cities in Korea from 2003 to 2008. We also explored potential effect modification by individual characteristics such as sex and age. We used hierarchical modeling to first estimate city-specific associations between heat, cold, or heat waves and hospitalizations, and then estimated overall effects. Stratified analyses were performed by cause of hospitalization, sex, and age (0–14, 15–64, 65–74, and ≥75 years). Cardiovascular hospitalizations were significantly associated with high temperature, whereas hospitalizations for allergic disease, asthma, and selected respiratory disease were significantly associated with low temperature. The overall heat effect for cardiovascular hospitalization was a 4.5 % (95 % confidence interval 0.7, 8.5 %) increase in risk comparing hospitalizations at 25 to 15 °C. For cold effect, the overall increase in risk of hospitalizations comparing 2 with 15 °C was 50.5 (13.7, 99.2 %), 43.6 (8.9, 89.5 %), and 53.6 % (9.8, 114.9 %) for allergic disease, asthma, and selected respiratory disease, respectively. We did not find statistically significant effects of heat waves compared with nonheat wave days. Our results suggest susceptible populations such as women and younger persons. Our findings provide suggestive evidence that both high and low ambient temperatures are associated with the risk of hospital admissions, particularly in women or younger person, in Korea.     

African ancestry is associated with risk of asthma and high total serum IgE in a population from the Caribbean Coast of Colombia

African descended populations exhibit an increased prevalence of asthma and allergies compared to Europeans. One approach to distinguish between environmental and genetic explanations for this difference is to study relationships of asthma risk to individual admixture. We aimed to determine the admixture proportions of a case-control sample from the Caribbean Coast of Colombia currently participating in genetic studies for asthma, and to test for population stratification and association between African ancestry and asthma and total serum IgE levels (tIgE). We genotyped 368 asthmatics and 365 non-asthmatics for 52 autosomal ancestry informative markers, six mtDNA haplogroups and nine haplogroups and five microsatellites in Y chromosome. Autosomal admixture proportions, population stratification, and associations between ancestry and the phenotypes were estimated by ADMIXMAP. The average admixture proportions among asthmatics were 42.8% European, 39.9% African and 17.2% Native American and among non-asthmatics they were 44.2% (P = 0.068), 37.6% (P = 0.007) and 18.1% (P = 0.050), respectively. In the total sample, the paternal contributions were 71% European, 25% African and 4.0% Native American and the maternal lineages were 56.8% Native American, and 20.2% African; 22.9% of the individuals carried other non-Native American mtDNA haplogroups. African ancestry was significantly associated with asthma (OR: 2.97; 95% CI: 1.08–8.08), high tIgE (OR: 1.9; 95% CI: 1.17–3.12) and socioeconomic status (OR = 0.64; 95% CI: 0.47–0.87). Significant population stratification was observed in this sample. Our findings indicate that genetic factors can explain the association between asthma and African ancestry and suggest that this sample is a useful resource for performing admixture mapping for asthma.     

Predicting phenotypes of asthma and eczema with machine learning

Background

There is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations.

Methods

The study population comprised a cross-sectional sample of adults, and included representatives of the general population enriched by subjects with asthma. Linear and non-linear machine learning methods, from logistic regression to random forests, were fit on a large attribute set including demographic, clinical and laboratory features, genetic profiles and environmental exposures. Outcome of interest were asthma, wheeze and eczema encoded by different operational definitions. Model validation was performed via bootstrapping.

Results

The study population included 554 adults, 42% male, 38% previous or current smokers. Proportion of asthma, wheeze, and eczema diagnoses was 16.7%, 12.3%, and 21.7%, respectively. Models were fit on 223 non-genetic variables plus 215 single nucleotide polymorphisms. In general, non-linear models achieved higher sensitivity and specificity than other methods, especially for asthma and wheeze, less for eczema, with areas under receiver operating characteristic curve of 84%, 76% and 64%, respectively. Our findings confirm that allergen sensitisation and lung function characterise asthma better in combination than separately. The predictive ability of genetic markers alone is limited. For eczema, new predictors such as bio-impedance were discovered.

Conclusions

More usefully-complex modelling is the key to a better understanding of disease mechanisms and personalised healthcare: further advances are likely with the incorporation of more factors/attributes and longitudinal measures.

     

Effects of Meteorological Factors on Daily Hospital Admissions for Asthma in Adults: A Time-Series Analysis

Background

There is limited evidence for the impacts of meteorological changes on asthma hospital admissions in adults in Shanghai, China.

Objectives

To quantitatively evaluate the short-term effects of daily mean temperature on asthma hospital admissions.

Methods

Daily hospital admissions for asthma and daily mean temperatures between January 2005 and December 2012 were analyzed. After controlling for secular and seasonal trends, weather, air pollution and other confounding factors, a Poisson generalized additive model (GAM) combined with a distributed lag non-linear model were used to explore the associations between temperature and hospital admissions for asthma.

Results

During the study periods, there were 15,678 hospital admissions for asthma by residents of Shanghai, an average 5.6 per day. Pearson correlation analysis found a significant negative correlation (r = −0.174, P<0.001) between asthma hospitalizations and daily mean temperature (DMT). The DMT effect on asthma increased below the median DMT, with lower temperatures associated with a higher risk of hospital admission for asthma. Generally, the cold effect appeared to be relatively acute, with duration lasting several weeks, while the hot effect was short-term. The relative risk of asthma hospital admissions associated with cold temperature (the 25^th percentile of temperature relative to the median temperature) was 1.20 (95% confidence interval [CI], 1.01∼1.41) at lag0-14. However, warmer temperatures were not associated with asthma hospital admissions.

Conclusions

Cold temperatures may trigger asthmatic attacks. Effective strategies are needed to protect populations at risk from the effects of cold.     

Trends in atrial fibrillation hospitalizations in the United States: A report using data from the National Hospital Discharge Survey

Aims

Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. Patients presenting with AF are often admitted to hospital for rhythm or rate control, symptom management, and/or anticoagulation. We investigated temporal trends in AF hospitalizations in United States from 1996 to 2010.

Bayesian Predictive Probability Functions for Count Data that are Subject to Misclassification

We develop three Bayesian predictive probability functions based on data in the form of a double sample. One Bayesian predictive probability function is for predicting the true unobservable count of interest in a future sample for a Poisson model with data subject to misclassification and two Bayesian predictive probability functions for predicting the number of misclassified counts in a current observable fallible count for an event of interest. We formulate a Gibbs sampler to calculate prediction intervals for these three unobservable random variables and apply our new predictive models to calculate prediction intervals for a real‐data example. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman’s correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March–April, while the peak day was predicted using the temperature during the last 10 days of March.     

Weather,season, and daily stroke admissions in Hong Kong

Previous studies examining daily temperature and stroke incidence have given conflicting results. We undertook this retrospective study of all stroke admissions in those aged 35 years old and above to Hong Kong public hospitals from 1999 through 2006 in order to better understand the effects of meteorological conditions on stroke risk in a subtropical setting. We used Poisson Generalized Additive Models with daily hemorrhagic (HS) and ischemic stroke (IS) counts separately as outcomes, and daily mean temperature, humidity, solar radiation, rainfall, air pressure, pollutants, flu consultation rates, day of week, holidays, time trend and seasonality as predictors. Lagged effects of temperature, humidity and pollutants were also considered. A total of 23,457 HS and 107,505 IS admissions were analyzed. Mean daily temperature had a strong, consistent, negative linear association with HS admissions over the range (8.2-31.8°C) observed. A 1°C lower average temperature over the same day and previous 4 days (lags 0-4) being associated with a 2.7% (95% CI: 2.0-3.4%, P < .0.0001) higher admission rate after controlling for other variables. This association was stronger among older subjects and females. Higher lag 0-4 average change in air pressure from previous day was modestly associated with higher HS risk. The association between IS and temperature was weaker and apparent only below 22°C, with a 1°C lower average temperature (lags 0-13) below this threshold being associated with a 1.6% (95% CI:1.0-2.2%, P < 0.0001) higher IS admission rate. Pollutant levels were not associated with HS or IS. Future studies should examine HS and IS risk separately.     

Designing new automatically generated pollen calendars for the public in Switzerland

Pollen calendars are one of the most comprehensible means to inform allergy sufferers or medical professionals about the mean presence of allergenic pollen during the course of the year. They have been produced with a variety of methods and were distributed with great success since the beginning of pollen monitoring. Current technologies, longer data series and changing user demands allow to develop new calculation methods. For designing the new pollen calendars of Switzerland, the following requirements were formulated: The pollen load levels in the calendars should correspond to the levels used in pollen forecasts. A pollen load level in the calendar should show the time window in which it potentially occurs. Further requirements were an automatic generation of the calendars, a regular update and the possibility to provide calendars for single stations, regions or specific pollen species. The data analysis is based on mean daily pollen concentrations of the last 20 years of all 14 pollen-monitoring stations in Switzerland for the 15 pollen types most relevant for allergies. For each day of the year, the 90% quantile of the daily pollen concentrations is determined in a moving 9-day time window over 20 years of data. The calculated concentrations are converted afterward into a pollen load level. The new method is flexible because various parameters can be selected freely: the reference period, the size of the moving time window, the quantile value and the thresholds for pollen load levels. Adjusting these parameters, also pollen calendars for fewer than 20 years can be calculated. However, a sensitivity analysis showed that a reference period of 20 years provides much more stable pollen calendars than shorter reference periods.     

Effects of diurnal variations in temperature on non-accidental mortality among the elderly population of Montreal,Québec, 1984–2007

The association between ambient temperature and mortality has been studied extensively. Recent data suggest an independent role of diurnal temperature variations in increasing daily mortality. Elderly adults—a growing subgroup of the population in developed countries—may be more susceptible to the effects of temperature variations. The aim of this study was to determine whether variations in diurnal temperature were associated with daily non-accidental mortality among residents of Montreal, Québec, who were 65 years of age and over during the period between 1984 and 2007. We used distributed lag non-linear Poisson models constrained over a 30-day lag period, adjusted for temporal trends, mean daily temperature, and mean daily concentrations of nitrogen dioxide and ozone to estimate changes in daily mortality with diurnal temperature. We found, over the 30 day lag period, a cumulative increase in daily mortality of 5.12 % [95 % confidence interval (CI): 0.02–10.49 %] for a change from 5.9 °C to 11.1 °C (25th to 75th percentiles) in diurnal temperature, and a 11.27 % (95%CI: 2.08–21.29 %) increase in mortality associated with an increase of diurnal temperature from 11.1 to 17.5 °C (75th to 99th percentiles). The results were relatively robust to adjustment for daily mean temperature. We found that, in Montreal, diurnal variations in temperature are associated with a small increase in non-accidental mortality among the elderly population. More studies are needed in different geographical locations to confirm this effect.     

Optimization of Microbial Leaching of Base Metals from a South African Sulfidic Nickel Ore Concentrate by Acidithiobacillus ferrooxidans

X-ray diffraction analysis revealed that pentlandite and chalcopyrite were the prominent mineral phases in a South African sulfidic nickel ore concentrate that hosted nickel and copper. Cobalt was found to be closely associated with the nickel-bearing pentlandite phase of the ore sample. Microbial batch leaching experiments designed according to a central composite design model were run for 15 days in a shaking incubator (150 rpm) at a constant temperature (30°C) with variations in experimental parameters like ore pulp density, particle size, bacterial inoculum, pH of the culture medium, and residence time. Quadratic mathematical models were developed to predict the rate of metal extractions. The suitability of the model of the microbial leaching process was confirmed from normal probability curves. An analysis of variance indicated that the residence time, pulp density of the ore, and particle size were the most significant factors. Bacterial inoculum size hardly showed any effect on the total metal extractions. Maximum nickel (82%), cobalt (76%) and copper (25.6%) extractions were achieved under optimum conditions, operated for 15 days at pulp density of 2% and particle size of ?75 µm at pH 1.5.     

Prediction of Asthma Exacerbations in Children by Innovative Exhaled Inflammatory Markers: Results of a Longitudinal Study

Background

In asthma management guidelines the primary goal of treatment is asthma control. To date, asthma control, guided by symptoms and lung function, is not optimal in many children and adults. Direct monitoring of airway inflammation in exhaled breath may improve asthma control and reduce the number of exacerbations.

Aim

1) To study the use of fractional exhaled nitric oxide (FeNO) and inflammatory markers in exhaled breath condensate (EBC), in the prediction of asthma exacerbations in a pediatric population. 2) To study the predictive power of these exhaled inflammatory markers combined with clinical parameters.

Methods

96 asthmatic children were included in this one-year prospective observational study, with clinical visits every 2 months. Between visits, daily symptom scores and lung function were recorded using a home monitor. During clinical visits, asthma control and FeNO were assessed. Furthermore, lung function measurements were performed and EBC was collected. Statistical analysis was performed using a test dataset and validation dataset for 1) conditionally specified models, receiver operating characteristic-curves (ROC-curves); 2) k-nearest neighbors algorithm.

Results

Three conditionally specified predictive models were constructed. Model 1 included inflammatory markers in EBC alone, model 2 included FeNO plus clinical characteristics and the ACQ score, and model 3 included all the predictors used in model 1 and 2. The area under the ROC-curves was estimated as 47%, 54% and 59% for models 1, 2 and 3 respectively. The k-nearest neighbors predictive algorithm, using the information of all the variables in model 3, produced correct predictions for 52% of the exacerbations in the validation dataset.

Conclusion

The predictive power of FeNO and inflammatory markers in EBC for prediction of an asthma exacerbation was low, even when combined with clinical characteristics and symptoms. Qualitative improvement of the chemical analysis of EBC may lead to a better non-invasive prediction of asthma exacerbations.     

Significant association between GSTT1 null genotype and risk of asthma during childhood in Caucasians

Asthma is a complex multifactorial disorder and its management requires a better understanding of its various pathogenesis and mechanisms. Previous studies assessing the association between glutathione S-transferase T1 (GSTT1) null genotype and asthma risk during childhood reported conflicting results. To get a more precise estimation of the association between GSTT1 null genotype and risk of asthma during childhood, we performed a meta-analysis of 16 studies with a total of 18,558 subjects. Subgroup analyses were performed by ethnicity. The pooled odds ratio (OR) with corresponding 95 % confidence interval (95 %CI) was used to assess the association. Overall, there was a significant association between GSTT1 null genotype and increased risk of children asthma (OR = 1.25, 95 % CI, 1.02–1.54; P = 0.032). Subgroup analyses showed GSTT1 null genotype was associated with increased risk of children asthma in Caucasians (OR = 1.46, 95 % CI, 1.04–2.03; P = 0.027), but not in Asians (OR = 1.03, 95 % CI, 0.55–1.94; P = 0.928) and Africans (OR = 1.33, 95 % CI, 0.92–1.91; P = 0.127). There was no evidence of publication bias in the subgroup analysis of Caucasians. In conclusion, there is a significant association between GSTT1 null genotype and risk of asthma during childhood in Caucasians. More well-designed epidemiological studies are needed to further assess this association in Asians and Africans.     

Is the computed speciation of copper in a wide range of Chinese soils reliable?

Free Cu species in soils is a key issue to its bioavailability. However, predictive models for Cu speciation across a wide range of soils were still unavailable. In this study, Cu speciation in 34 contaminated soil samples were investigated via analytical technique and predictive models. The results showed that most of free Cu²⁺ was underestimated when using default log K_CuFA and 65% active fulvic acid as inputs in models of WHAM VI and NICA-Donnan. The best prediction was found when using either adjusted active fulvic acid from 10% to 125% for WHAM VI or from 15% to 65% for NICA-Donnan model with the RMSE < 0.32 and r² > 0.96. In contrast, NICA-Donnan demonstrated a slightly stronger binding for Cu than WHAM VI due to extra 26% of samples was underestimated. This work presents a comprehensive database of Cu speciation and an effective attempt of free Cu²⁺ prediction in a wide range of Chinese soils.     

Use of “Cold Spell” indices to quantify excess chronic obstructive pulmonary disease (COPD) morbidity during winter (November to March 2000–2007): case study in Porto

The aim of this study was to examine the relationship between the occurrence of cold episodes and excess hospital admissions for chronic obstructive pulmonary disease (COPD) in Porto, Portugal, in order to further understand the effects of cold weather on health in milder climates. Excess COPD winter morbidity was calculated from admissions for November to March (2000–2007) in the Greater Porto Metropolitan Area (GPMA). Cold spells were identified using several indices (Díaz, World Meteorological Organization, Cold Spell Duration Index, Australian Index and Ondas’ Project Index) for the same period. Excess admissions in the periods before and after the occurrence of cold spells were calculated and related to the cold spells identified. The COPD seasonal variation admission coefficient (CVSA) showed excess winter admissions of 59 %, relative to other months. The effect of cold spell on the aggravation of COPD occurs with a lag of at least 2 weeks and differs according to the index used. This study indicates the important role of the persistence of cold periods of at least 2 weeks duration in the increase in COPD admissions. The persistence of moderate temperatures (Tmin ≤5 °C) for a week can be more significant for increasing COPD admissions than very low temperatures (Tmin?≤?1.6 °C) for just a few days. The Ondas projects’ index provides the most accurate detection of the negative impacts of cold persistency on health, while the Diaz index is better at evaluating the consequences of short extreme cold events.