Daily hospital respiratory admissions and winter air mass types, Birmingham, UK

 A synoptic climatological approach is used to investigate linkages between air mass types (weather situations), the daily mean particulate matter with a size of 10 μm or less (PM10) concentrations and all respiratory hospital admissions for the Birmingham area, UK. Study results show distinct differential responses of respiratory admission rates to the six winter air mass types identified. Two of the three air masses associated with above average admission rates (continental anticyclonic gloom and continental anticyclonic fine and cold) also favour high PM10 levels. This association is suggestive of a possible linkage between weather, air quality and health. The remaining admissions-sensitive air mass type (cool moist maritime) does not favour high PM10 levels. This is considered to be indicative of a direct weather-health relationship. A sensitising mechanism is proposed to account for the linkages between air mass type, PM10 concentrations and respiratory response. Received: 4 August 1997 / Received after revision: 8 January 1999 / Accepted: 20 January 1999     

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.     

Winter circulation weather types and hospital admissions for respiratory diseases in Galicia,Spain

The link between various pathologies and atmospheric conditions has been a constant topic of study over recent decades in many places across the world; knowing more about it enables us to pre-empt the worsening of certain diseases, thereby optimizing medical resources. This study looked specifically at the connections in winter between respiratory diseases and types of atmospheric weather conditions (Circulation Weather Types, CWT) in Galicia, a region in the north-western corner of the Iberian Peninsula. To do this, the study used hospital admission data associated with these pathologies as well as an automatic classification of weather types. The main result obtained was that weather types giving rise to an increase in admissions due to these diseases are those associated with cold, dry weather, such as those in the east and south-east, or anticyclonic types. A second peak was associated with humid, hotter weather, generally linked to south-west weather types. In the future, this result may help to forecast the increase in respiratory pathologies in the region some days in advance.     

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.     

Environmental Factors Related to Pulmonary Tuberculosis in HIV-Infected Patients in the Combined Antiretroviral Therapy (cART) Era

The aim of our study was to evaluate the seasonal variations and whether short-term exposure to environmental risk factors, such as climate and air pollution, is associated with PTB-related hospital admissions in human immunodeficiency virus (HIV)-infected patients in Spain during the era of combined antiretroviral therapy (cART). A retrospective study was carried out using data from the Minimum Basic Data Set (MBDS) and the State Meteorological Agency (AEMET) of Spain. The primary outcome variable was hospital admissions with PTB diagnosis. The environmental risk factors evaluated were season, temperature, humidity, NO₂, SO₂, O₃, PM₁₀, and CO. Overall, HIV-infected patients had a lower frequency of PTB-related hospital admissions in summer (22.8%) and autumn (22.4%), but higher values in winter (26.6%) and spring (28.2%). Using a Bayesian temporal model, PTB-related hospital admissions were less frequent in summer-autumn and more abundant in winter-spring during the first years of follow-up. During the later years of follow-up, the seasonal trends continued resulting in the lowest values in autumn and the highest in spring. When considering short-term exposure to environmental risk factors, lower temperatures at 1 week (odds ratio (OR) = 1.03; p = 0.008), 1.5 weeks (OR = 1.03; p<0.001), 2 weeks (OR = 1.04; p<0.001), and 3 weeks (OR = 1.03; p<0.001) prior to PTB admission. In addition, higher concentration of NO₂ at the time of admission were significantly associated with higher likelihoods of PTB-related hospital admission in HIV-infected patients when 1.5 weeks (OR = 1.1; p = 0.044) and 2 weeks (OR = 1.21; p<0.001) were used as controls. Finally, higher concentration of SO₂ at 1.5 weeks prior to PTB admission was significantly associated with a higher likelihood of PTB-related hospital admissions (OR = 0.92; p = 0.029). In conclusion, our data suggest an apparent seasonal variation in hospital admissions of HIV-infected patients with a PTB diagnosis (summer/autumn vs. winter/spring), as well as a link to short-term exposure to environmental risk factors, such as temperature and ambient NO₂ and SO₂.     

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.     

The effects of the 2009 dust storm on emergency admissions to a hospital in Brisbane, Australia

In September 2009 an enormous dust storm swept across eastern Australia. Dust is potentially hazardous to health as it interferes with breathing, and previous dust storms have been linked to increased risks of asthma and even death. We examined whether the 2009 Australian dust storm changed the volume or characteristics of emergency admissions to hospital. We used an observational study design, using time series analyses to examine changes in the number of admissions, and case-only analyses to examine changes in the characteristics of admissions. The admission data were from the Prince Charles Hospital, Brisbane, between 1 January 2009 and 31 October 2009. There was a 39% increase in emergency admissions associated with the storm (95% confidence interval: 5, 81%), which lasted for just 1 day. The health effects of the storm could not be detected using particulate matter levels. We found no significant change in the characteristics of admissions during the storm; specifically, there was no increase in respiratory admissions. The dust storm had a short-lived impact on emergency hospital admissions. This may be because the public took effective avoidance measures, or because the dust was simply not toxic, being composed mainly of soil. Emergency departments should be prepared for a short-term increase in admissions during dust storms.     

Association of meteorological and day-of-the-week factors with emergency hospital admissions in Fukuoka, Japan

We carried out a statistical study of the influence of meteorological and day-of-the-week factors on the intrinsic emergency patients transported to hospitals by ambulance. Multiple piecewise linear regression analysis was performed on data from 6,081 emergency admissions for 1 year between April 1997 and March 1998 in Fukuoka, Japan. The response variable was the daily number of emergency patients admitted with three types of disease: cerebrovascular, respiratory and digestive diseases. The results showed that the number of emergency patients admitted daily with cerebrovascular disease was significantly associated with temperature on the day of admission and whether the day was Sunday. As it became colder than 12 degrees C, emergency admissions of patients with cerebrovascular disease increased drastically, reaching a plateau at 4 degrees C. On the 3rd and 7th days after the temperature fell below 10 degrees C, the daily admission of patients with respiratory disease significantly increased. We also observed a weak association between emergency admissions of patients suffering from digestive disease and rising barometric pressure on the day of admission.     

Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir,China

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.     

Acute and chronic effects of particles on hospital admissions in New-England

Background

Many studies have reported significant associations between exposure to PM_2.5 and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of PM_2.5 monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias.

Methods

Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM_2.5 exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM_2.5 exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk.

Results

We observed associations between both short-term and long-term exposure to PM_2.5 and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-µg/m³ increase in short-term PM_2.5 exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-µg/m³ increase in long-term PM_2.5 exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75).

Conclusions

As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.     

Short-term effects of pollen species on hospital admissions in the city of Madrid in terms of specific causes and age

Background In recent years there has been a notable increase in respiratory diseases in industrialised countries, which is attributed to a combination of chemical atmospheric pollution and the allergens existing in the atmosphere of big cities. Few studies, however, have analysed the effect of different pollen species on the different causes of hospital admissions other than those exclusively owing to asthma. Objective The aim of this investigation was to analyse the influence of the most abundant pollen species with the highest allergenic potential in Madrid’s atmosphere on daily emergency hospital admissions – from all causes and specific causes – according to different age groups. Methods An ecological time-series design was adopted in which the effects were quantified using Poisson regression models, taking into account different confusion factors, such as chemical and acoustic atmospheric pollution. Results Statistically significant associations were found between pollen species and hospital admissions due to respiratory causes, and between pollen species and all causes of hospital admissions and, to a lesser degree, circulatory causes. The impact was greater in the younger age groups. Concentrations of Poaceae and Platanus pollen species were the factors showing the highest correlation to the different causes of admission. Conclusion The relative risks analysis revealed a significant effect between the pollen species analysed and health for admitted patients of all age groups; this effect was greater than that detected for the environmental variables traditionally analysed in urban atmospheres.     

Forecasting peak asthma admissions in London: an application of quantile regression models

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.     

The Effect of a Smoking Ban on Hospitalization Rates for Cardiovascular and Respiratory Conditions in Prince Edward Island,Canada

Background

This is the first study to have examined the effect of smoking bans on hospitalizations in the Atlantic Canadian socio-economic, cultural and climatic context. On June 1, 2003 Prince Edward Island (PEI) enacted a province-wide smoking ban in public places and workplaces. Changes in hospital admission rates for cardiovascular (acute myocardial infarction, angina, and stroke) and respiratory (chronic obstructive pulmonary disease and asthma) conditions were examined before and after the smoking ban.

Methods

Crude annual and monthly admission rates for the above conditions were calculated from April 1, 1995 to December 31, 2008 in all PEI acute care hospitals. Autoregressive Integrated Moving Average time series models were used to test for changes in mean and trend of monthly admission rates for study conditions, control conditions and a control province after the comprehensive smoking ban. Age- and sex-based analyses were completed.

Results

The mean rate of acute myocardial infarctions was reduced by 5.92 cases per 100,000 person-months (P = 0.04) immediately after the smoking ban. The trend of monthly angina admissions in men was reduced by −0.44 cases per 100,000 person-months (P = 0.01) in the 67 months after the smoking ban. All other cardiovascular and respiratory admission changes were non-significant.

Conclusions

A comprehensive smoking ban in PEI reduced the overall mean number of acute myocardial infarction admissions and the trend of angina hospital admissions.     

Effects of Age,Sex, Index Admission,and Predominant Polarity on the Seasonality of Acute Admissions For Bipolar Disorder: A Population-Based Study

Bipolar disorder seasonality has been documented previously, though information on the effect of demographic and clinical variables on seasonal patterns is scant. This study examined effects of age, sex, index admission, and predominant polarity on bipolar disorder seasonality in a nationwide population. An inpatient cohort admitted to hospital exclusively for mental illness was derived from the Taiwan National Health Insurance Research Database for 2002–2007. The authors identified 9619 inpatients with bipolar disorder, who had generated 15 078 acute admission records. An empirical mode decomposition method was used to identify seasonal oscillations in bipolar admission data, and regression and cross-correlation analyses were used to quantify the degree and timing of bipolar admission seasonality. Results for seasonality timing found that manic or mixed episodes peak in spring or summer, and depressive episodes peak in winter. Analysis for degree of seasonality revealed that (1) the polarity of patients' index admission predicted the seasonality of relapse admissions; (2) seasonality was significant in female admissions for depressive episodes and in male admissions for manic episodes; (3) young adults displayed a higher degree of seasonality for acute admissions than middle-aged adults; and (4) patients with predominantly depressive admissions displayed a higher degree of seasonality than patients with predominantly manic admissions. Demographic and clinical variables were found to affect the seasonality of acute admissions for bipolar disorders. These findings highlight the need for research on identification and management of seasonal features in bipolar patients. (Author correspondence: ccyang@physionet.org)     

Improving phenological forecasting models for rape stem weevil,Ceutorhynchus napi Gyll., based on long‐term multisite datasets

New threshold‐based models to predict the start of invasion by the stem‐boring pest, the rape stem weevil (Ceutorhynchus napi Gyll.) of winter oilseed rape (Brassica napus L.), were developed and compared to published models using long‐term datasets on weather and weevil phenology from experimental locations in Germany and Luxembourg. Threshold values for daily records of maximum air temperature, mean soil temperature, sunshine duration and total precipitation were adjusted to local conditions on the date of first weevil migration in spring. Mean error and the root mean squared error were used to assess model quality, where the error is defined as the number of days between predicted and observed arrival of weevils on the crop (regardless of sign). Best model results predicted first crop invasion by rape stem weevil when the thresholds of daily maximum air temperature ≥7.8°C, mean soil temperature ≥6.6°C, daily total precipitation ≤1.0 mm and sunshine duration ≥1 h were matched. This model takes into account meteorological variables likely to influence conditions at the overwintering site of the weevils in the soil, as well as variables that may limit weevil flight. Adjusted air temperature threshold values were consistently lower for Luxembourg sites than for those optimized for Germany. A simple model relating the date of first weevil invasion to accumulated daily maximum air temperature above 0°C (from 1 January) was also evaluated. This proved less suitable for forecasting crop invasion by C. napi. We suggest that phenological models using locally adjusted meteorological‐based thresholds have the potential to offer sufficiently accurate forecasts of first immigration flights by C. napi for appropriate timing of insecticide application. In addition, the developed models are suitable tools to be used in climate change impact studies.     

Short-term effects of air pollution,markers of endothelial activation,and coagulation to predict major adverse cardiovascular events in patients with acute coronary syndrome: insights from AIRACOS study

The aim of this study was to determine whether markers of inflammation and coagulation are associated with short-term particulate matter exposure and predict major adverse cardiovascular events at 360 d in patients with acute coronary syndrome (ACS). We included 307 consecutive patients, and assessed the average concentrations of data on atmospheric pollution in ambient air and meteorological variables from 1?d up to 7 d prior to admission. In patients with ACS, the markers of endothelial activation and coagulation, but not black carbon exposure, are associated with major adverse cardiovascular events at one-year follow-up.     

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.     

Long-range ozone transport and its impact on respiratory and cardiovascular health in the north of Portugal

Ozone dynamics depend on meteorological characteristics such as wind, radiation, sunshine, air temperature and precipitation. The aim of this study was to determine ozone trajectories along the northern coast of Portugal during the summer months of 2005, when there was a spate of forest fires in the region, evaluating their impact on respiratory and cardiovascular health in the greater metropolitan area of Porto. We investigated the following diseases, as coded in the ninth revision of the International Classification of Diseases: hypertensive disease (codes 401–405); ischemic heart disease (codes 410–414); other cardiac diseases, including heart failure (codes 426–428); chronic obstructive pulmonary disease and allied conditions, including bronchitis and asthma (codes 490–496); and pneumoconiosis and other lung diseases due to external agents (codes 500–507). We evaluated ozone data from air quality monitoring stations in the study area, together with data collected through HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model analysis of air mass circulation and synoptic-scale zonal wind from National Centers for Environmental Prediction data. High ozone levels in rural areas were attributed to the dispersion of pollutants induced by local circulation, as well as by mesoscale and synoptic scale processes. The fires of 2005 increased the levels of pollutants resulting from the direct emission of gases and particles into the atmosphere, especially when there were incoming frontal systems. For the meteorological case studies analyzed, peaks in ozone concentration were positively associated with higher rates of hospital admissions for cardiovascular diseases, although there were no significant associations between ozone peaks and admissions for respiratory diseases.     

Prediction of mortality in very premature infants: a systematic review of prediction models

Context

Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models.

Methods

Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies.

Results

We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data.

Conclusions

Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.