Quantifying the Effects of Climate Factors on Carbon Monoxide Poisoning: A Retrospective Study in Taiwan

Associations of ambient temperature with the CO poisoning risk in China

Although studies have explored the relationship between temperature and CO poisoning, the results are not consistent, and there is still a lack of early warning criteria of temperature related to CO poisoning. In order to comprehensively study the exposure-response relationship between daily average temperature and CO poisoning, and to further explore the early warning criteria of temperature related to CO poisoning, we used daily cases of CO poisoning in 31 National Injury Surveillance System (NISS) surveillance sites in seven administrative geographical regions of China and daily meteorological data obtained from the China Meteorological Science Data Sharing Service Platform from 2009 to 2019 to do the analysis. Daily meteorological data of 698 weather stations across China were interpolated at a 0.01° × 0.01°spatial resolution, which were then applied to extract the daily meteorological data of all included NISS sites. The Distributed Lag Non-linear Model (DLNM) model was applied to estimate the exposure-response associations (relative risk, RR) of daily mean temperature with CO poisoning, which was then further used to identify early warning criteria of temperature related to CO poisoning. A total of 10,618 CO poisoning cases were included in this study, with an average of 0.4 cases per day. There was generally a reverse J-shaped association between temperature and CO poisoning risk, indicating that both low and high temperature may increase the risk of CO poisoning, but low temperature usually has a longer lagged effects than high temperature. Spatialy, the exposure-response associations between temperatue and CO poisoning largely varied among regions, with greater effects of low temperatures in Southern China than in Northern China. The cumulative effects (RR, lag0-6 days) of 10 % percentile temperature ranged from 1.13 (95%CI: 1.01,1.26) in East China to 1.73 (95%CI:1.63,1.83) in South China. We also observed significant spatial variations in the early warning criteria of temperature related to CO poisoning across China. However, the patterns of high temperature effects on CO poisoning and the warning criteria of high temperature were mixed across China. In conclusions, both low temperature and high temperature may increase the risk of CO poisoning in China, and the effect of low temperature is more obvious, especially in South China, Northeast China, and North China. In addition, there is an urgent need to establish air temperature early warning and grading criteria for CO poisoning in different areas of China.

An evaluation of childhood carbon monoxide intoxications in a rural area using the Beaufort wind scale

Climatic changes are known to affect CO intoxications. The purpose of this study was to examine childhood CO intoxications with the Beaufort wind scale (BWS) classification of wind speeds. The demographic data (age and sex) and information concerning the hour, day, and month of presentation to the emergency department for cases diagnosed with CO intoxication over a 7-year period between 2015 and 2021 in the pediatric emergency department of a tertiary training and research hospital in a rural area were examined. Wind speeds (m/s) measured on the days of presentation to the emergency department were recorded. The wind category on the BWS on the day of intoxication was then determined. Four hundred twenty-two patients, with a mean age of 95.12 ± 59.4 (1-215) months, 218 (51.7%) girls and 204 (48.3%) boys were diagnosed with CO intoxication over the 7-year study period. A comparison of wind speeds on the days of presentation to hospital revealed a significantly higher wind speed in 2020 than in the other years (p<0.001). A comparison of the groups in terms of the BWS revealed a significant difference between the years of presentation to hospital (p:0.001). This is the first study to investigate CO intoxications in the pediatric emergency department with the BWS. A significant association was observed between wind speed based on the BWS and childhood CO intoxications. Further studies evaluating wind in the rural setting and CO intoxications are now needed for protection against such intoxications.

Quantifying the effects of cold waves on carbon monoxide poisoning: A time-stratified case-crossover study in Jinan, China

Background

Previous studies have shown that carbon monoxide (CO) poisoning occurs mostly in winter and is associated with severe cold weather (e.g., ice storms, temperature drops). However, according to previous studies, the impact of low temperature on health has a delayed effect, and the existing research cannot fully reveal the delayed effect of cold waves on CO poisoning.

Objectives

The purpose of this study is to analyze the temporal distribution of CO poisoning in Jinan and to explore the acute effect of cold waves on CO poisoning.

Methods

We collected emergency call data for CO poisoning in Jinan from 2013 to 2020 and used a time-stratified case-crossover design combined with a conditional logistic regression model to evaluate the impact of the cold wave day and lag 0-8 days on CO poisoning. In addition, 10 definitions of a cold wave were considered to evaluate the impact of different temperature thresholds and durations.

Results

During the study period, a total of 1,387 cases of CO poisoning in Jinan used the emergency call system, and more than 85% occurred in cold months. Our findings suggest that cold waves are associated with an increased risk of CO poisoning in Jinan. When P01, P05, and P10 (P01, P05, and P10 refer to the 1st, 5th, and 10th percentiles of the lowest temperature, respectively) were used as temperature thresholds for cold waves, the most significant effects (the maximum OR value, which refers to the risk of CO poisoning on cold wave days compared to other days) were 2.53 (95% CI:1.54, 4.16), 2.06 (95% CI:1.57, 2.7), and 1.49 (95% CI:1.27, 1.74), respectively.

Conclusion

Cold waves are associated with an increased risk of CO poisoning, and the risk increases with lower temperature thresholds and longer cold wave durations. Cold wave warnings should be issued and corresponding protective policies should be formulated to reduce the potential risk of CO poisoning.

Validation of Diagnostic Codes to Identify Carbon Monoxide Poisoning in Taiwan’s Claims Data

Purpose: Previous studies identified the study cohort or outcome of carbon monoxide poisoning (COP) by using the relevant disease diagnosis codes in secondary databases, but the validity of diagnosis codes of COP is unclear in such secondary databases. This study aimed to evaluate the accuracy of case definitions for COP using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in Taiwan’s health insurance claims data.

Methods: We selected a 10% simple random sample from an original cohort of patients newly undergoing carboxyhemoglobin (COHb) testing under any clinical diagnosis at four Chang Gung Memorial Hospitals in Taiwan during 2011–2020. Two clinical doctors independently ascertained the COP diagnosis by reviewing the medical records as the reference standard. We estimated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of various case definitions (e.g., ICD-9-CM code, ICD-10-CM code and the uses of hyperbaric oxygen therapy) in the claims data.

Results: We randomly selected 557 cases from the original cohort of 5,571 cases newly receiving COHb test in the study hospitals. We found 90, 35, and 9 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 278 cases with an ICD-9-CM code of 986. A further 111, 34, and 6 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 279 cases with an ICD-10-CM code of T58. Using ICD-9-CM codes, the sensitivity, specificity, PPV and NPV for COP were 90.9, 80.4, 72, and 94.1%, respectively. Using ICD-10-CM codes they were 94.9, 79, 76.6, and 95.5%, respectively. PPV typically increased when COP was the primary diagnosis and could reach 100% if patients with ICD-CM code 986 or T58 also received hyperbaric oxygen therapy during hospitalization.

Conclusion: The COP-related ICD-CM codes alone did not accurately identify COP patients, but accuracy improved after including oxygen therapy data with the ICD-CM codes in Taiwan’s claims data.

Exploring Health Impacts of Occupational Exposure to Carbon Monoxide in the Labour Community of Hattar Industrial Estate

This study was designed to assess the health impacts related to noninvasive carbon monoxide saturation (SPCO %) in the blood of respondents. For this purpose, 150 respondents from the labour community of Hattar Industrial Estate (testing site) and 100 respondents from Sultan Pur (control site) were selected. To achieve this objective, a Rad-57 Pulse CO-Oximeter was used for noninvasive carboxyhemoglobin measurement. Carbon monoxide saturation (SPCO%) in the blood of respondents from Hattar Industrial Estate, Haripur, Pakistan has been compared with the WHO’s standard concentration of SPCO% (5%). High saturation of carbon monoxide (carboxyhemoglobin SPCO) in the blood of respondents and disease association have been interpreted in graphs formed on the basis of statistical analysis in terms of frequencies, using statistical software (SPSS), based on demographic entries as well as exposure time of the employees in the processing, food and steel industries. The highest SPCO% measured was 17% in the steel industry and the lowest measured level was 4.2%. Frequencies and percentages of respiratory inflammation, dermatosis, asthma, breathing issues and eye inflammation among respondents were 29%, 35%, 16.7%, 23.5% and 9%, respectively. Prevalence of disease in three different groups of respondents (from three testing sites) was also analyzed on the basis of exposure time (hrs.) to carbon monoxide emissions. Prevalence of disease among the exposed and non-exposed groups was analyzed and showed comparatively lower disease prevalence in the group of respondents who were not exposed to high carbon monoxide emissions. The data of the current study was also subjected to statistical modelling to find the health risk of air pollutants (carbon monoxide) on population health by calculating attributable risk (AR) or attributable proportion (AP). Results indicated that attributable risk of carbon monoxide exposure for respiratory diseases, dermatosis and eye inflammation were 61.12%, 65.77% and 24.95% respectively. Findings of statistical modelling indicated that dermatosis and respiratory diseases were more prevalent in laborers of industrial units than those at control site.