Dataset Information

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis.

ABSTRACT: To explore the association between weather conditions and hospital admissions for pneumonia in Shanghai.A time-series analysis was performed for a period of 4?years (January 2008-December 2011). A generalised additive model was used to calculate the relative risks.Shanghai, China.All daily hospital admissions for pneumonia were obtained from the Shanghai health insurance system between 1 January 2008 and 31 December 2011 (n=99?403).The relationship between the mean temperature and pneumonia hospital admissions followed a V-shaped curve, with an optimum temperature (OT) at 13°C. When the mean temperature was below the OT, a 1°C decrease corresponded to a 4.88% (95% CI 2.71% to 7.09%) and 5.34% (95% CI 2.04% to 8.74%) increase in pneumonia hospital admissions in lag 4 using a single-day lag structure and lag 0-7 using a multiday lag structure. When the mean temperature ?OT, no adverse effects from the temperature on pneumonia hospital admissions were found. The magnitude of the effects of temperature varied across gender and age groups. Hospitalisations for pneumonia increased by 15.99% (95% CI 0.06% to 34.46%) in the cold period.Cold temperature may be one of the important risk factors for pneumonia hospitalisations. Prevention programmes are needed to reduce the impact of cold temperature on pneumonia hospitalisations such as developing a weather warning system within a wide public health context.

SUBMITTER: Liu Y

PROVIDER: S-EPMC4091538 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis.

Liu Yuan Y Kan Haidong H Xu Jianming J Rogers David D Peng Li L Ye Xiaofang X Chen Renjie R Zhang Yue Y Wang Weibing W

BMJ open 20140701 7

<h4>Objectives</h4>To explore the association between weather conditions and hospital admissions for pneumonia in Shanghai.<h4>Design</h4>A time-series analysis was performed for a period of 4 years (January 2008-December 2011). A generalised additive model was used to calculate the relative risks.<h4>Setting</h4>Shanghai, China.<h4>Participants</h4>All daily hospital admissions for pneumonia were obtained from the Shanghai health insurance system between 1 January 2008 and 31 December 2011 (n=9 ...[more]

PMID: 24989619

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Project description:BACKGROUND:Children are especially vulnerable to pneumonia and the effects of air pollution. However, little is known about the impacts of air pollutants on pediatric admissions for Mycoplasma pneumonia. This study was conducted to investigate the impacts of air pollutants on pediatric hospital admissions for Mycoplasma pneumonia in Shanghai, China. METHODS:A cross-sectional design was applied to explore the association between pediatric hospital admissions and levels of air pollutants (fine particulate matter, particulate matter, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide). Data on hospital admissions for pneumonia and levels of ambient air pollutants were obtained for the period of 2015 to 2018. Associations between pediatric admissions for Mycoplasma pneumonia and ambient air pollutants were calculated using logistic regression and described by the odds ratio and relevant 95% confidence interval. The hysteresis effects of air pollutants from the day of hospital admission to the previous 7?days were evaluated in single-pollutant models and multi-pollutant models with adjustments for weather variables and seasonality. Lag 0 was defined as the day of hospital admission, lag 1 was defined as the day before hospital admission, and so forth. RESULTS:In the single-pollutant models (without adjustment for other pollutants), pediatric hospital admissions for pneumonia were positively associated with elevated concentrations of nitrogen dioxide and fine particulate matter. A 0.5% increase in daily admissions per 10-?g/m3 increase in the nitrogen dioxide level occurred at lag 1 and lag 2, and a 0.3% increase in daily admissions per 10-?g/m3 increase in fine particulate matter occurred at lag 1. In the multi-pollutant models, nitrogen dioxide and fine particulate matter remained significant after inclusion of particulate matter, ozone, sulfur dioxide, and carbon monoxide. CONCLUSIONS:This study illustrated that higher levels of nitrogen dioxide and fine particulate matter increase the risk of pediatric hospitalization for Mycoplasma pneumonia in Shanghai, China. These findings imply that the high incidence of Mycoplasma pneumonia in children in Asia might be attributed to the high concentration of specific air pollutants in Asia.

Project description:BACKGROUND:The effects of ambient particulate matter (PM) pollution on pneumonia in adults are inconclusive, and few scientific data on a national scale have been generated in low- or middle-income countries, despite their much higher PM concentrations. We aimed to examine the association between PM levels and hospital admissions for pneumonia in Chinese adults. METHODS AND FINDINGS:A nationwide time series study was conducted in China between 2014 and 2017. Information on daily hospital admissions for pneumonia for 2014-2017 was collected from the database of Urban Employee Basic Medical Insurance (UEBMI), which covers 282.93 million adults. Associations of PM concentrations and hospital admissions for pneumonia were estimated for each city using a quasi-Poisson regression model controlling for time trend, temperature, relative humidity, day of the week, and public holiday and then pooled by random-effects meta-analysis. Meta-regression models were used to investigate potential effect modifiers, including cities' annual-average air pollutants concentrations, temperature, relative humidity, gross domestic product (GDP) per capita, and coverage rates by the UEBMI. More than 4.2 million pneumonia admissions were identified in 184 Chinese cities during the study period. Short-term elevations in PM concentrations were associated with increased pneumonia admissions. At the national level, a 10-?g/m3 increase in 3-day moving average (lag 0-2) concentrations of PM2.5 (PM ?2.5 ?m in aerodynamic diameter) and PM10 (PM ?10 ?m in aerodynamic diameter) was associated with 0.31% (95% confidence interval [CI] 0.15%-0.46%, P < 0.001) and 0.19% (0.11%-0.30%, P < 0.001) increases in hospital admissions for pneumonia, respectively. The effects of PM10 were stronger in cities with higher temperatures (percentage increase, 0.031%; 95% CI 0.003%-0.058%; P = 0.026) and relative humidity (percentage increase, 0.011%; 95% CI 0%-0.022%; P = 0.045), as well as in the elderly (percentage increase, 0.10% [95% CI 0.02%-0.19%] for people aged 18-64 years versus 0.32% [95% CI 0.22%-0.39%] for people aged ?75 years; P < 0.001). The main limitation of the present study was the unavailability of data on individual exposure to PM pollution. CONCLUSIONS:Our findings suggest that there are significant short-term associations between ambient PM levels and increased hospital admissions for pneumonia in Chinese adults. These findings support the rationale that further limiting PM concentrations in China may be an effective strategy to reduce pneumonia-related hospital admissions.

Project description:BACKGROUND:Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation. OBJECTIVE:The aim of this study was to examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. METHODS:We applied 3 pattern mining algorithms to 680,138 medication administration records from 30,512 childhood inpatients with diagnosis of pneumonia during a 6-year period at a children's hospital in China. Patients' ages ranged from 0 to 17 years, where 37.53% (11,453/30,512) were 0 to 3 months old, 86.55% (26,408/30,512) were under 5 years, 60.37% (18,419/30,512) were male, and 60.10% (18,338/30,512) had a hospital stay of 9 to 15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first 2 are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these 3 methods identified the top 10 patterns from 6 age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (73.66%, 500,982/680,138) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize 5 medication use and 2 treatment patterns. RESULTS:We identified 5 medication use patterns: (1) antiasthmatics and expectorants and corticosteroids, (2) antibiotics and (antiasthmatics or expectorants or corticosteroids), (3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics, (4) antibiotics and (medications for enteritis or skin diseases), and (5) (antiasthmatics or expectorants or corticosteroids) and (medications for enteritis or skin diseases). We also identified 2 frequent treatment patterns: (1) 42.89% (291,701/680,138) of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements and (2) 11.53% (78,390/680,138) were of various combinations of inhalation of antiasthmatics, expectorants, or corticosteroids. Fleiss kappa for the subject experts' evaluation was 0.693, indicating moderate agreement. CONCLUSIONS:Utilizing a pattern mining approach, we summarized 5 medication use patterns and 2 treatment patterns. These warrant further investigation.

Project description:Background: Exposures to both ambient fine particulate matter (PM2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM2.5 concentration was 55.0 µg/m3. Each 10 µg/m3 increase in PM2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM2.5 and SWT, statistically significant interactions were found between PM2.5 and cold weather, with risk for PM2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM2.5 in the 1-3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

Project description:Many studies have evaluated associations between asthma emergency department (ED) visits, hospital admissions (HAs), and ambient ozone (O3) across the US, but not in Texas. We investigated the relationship between O3 and asthma HAs, and the potential impacts of outdoor pollen, respiratory infection HAs, and the start of the school year in Texas.We obtained daily time-series data on asthma HAs and ambient O3 concentrations for Dallas, Houston, and Austin, Texas for the years 2003-2011. Relative risks (RRs) and 95% confidence intervals (CIs) of asthma HAs per 10-ppb increase in 8-h maximum O3 concentrations were estimated from Poisson generalized additive models and adjusted for temporal trends, meteorological factors, pollen, respiratory infection HAs, day of the week, and public holidays. We conducted a number of sensitivity analyses to assess model specification.We observed weak associations between total asthma HAs and O3 at lags of 1 day (RR10 ppb = 1.012, 95% CI: 1.004-1.021), 2 days (RR10 ppb = 1.011, 95% CI: 1.002-1.019), and 0-3 days (RR10 ppb = 1.017, 95% CI: 1.005-1.030). The associations were primarily observed in children aged 5-14 years (e.g., for O3 at lag 0-3 days, RR10 ppb = 1.037, 95% CI: 1.011-1.064), and null in individuals 15 years or older. The effect estimates did not change significantly with adjustment for pollen and respiratory infections, but they attenuated considerably and lost statistical significance when August and September data were excluded. A significant interaction between time around the start of the school year and O3 at lag 2 day was observed, with the associations with pediatric asthma HAs stronger in August and September (RR10 ppb = 1.040, 95% CI: 1.012-1.069) than in the rest of the year (October-July) (RR10 ppb = 1.006, 95% CI: 0.986-1.026).We observed small but statistically significant positive associations between total and pediatric asthma HAs and short-term O3 exposure in Texas, especially in August and September. Further research is needed to determine how the start of school could modify the observed association between O3 and pediatric asthma HAs.

Dataset Information

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis.

Publications

Temporal relationship between hospital admissions for pneumonia and weather conditions in Shanghai, China: a time-series analysis.

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