-
Guangdong province was selected as our study site. Located in southern China, it has an area of approximately 179.8 thousand square kilometers, occupies approximately 1.87% of the country’s land area, and had a population of 115 million people in 2020. The entire territory is located between 20°09'−25°31' north latitude and 109°45'–117°20' east longitude, and the Tropic of Cancer traverses the central part of the province. Guangdong province has a subtropical climate characterized by a uniform temperature and high humidity. Summer is long, and winter is short. The average annual temperature is 21.8 ℃. Guangdong does not receive snow all year round. Except in extreme weather conditions, the temperature in Shaoguan city, the northernmost city of Guangdong, can be below 0 ℃. The economic development of Guangdong province varies; for example, the Pearl River Delta is an economically developed region, whereas northern and western Guangdong are relatively poor.
-
Data on cases of COP were collected from the home page data on hospital records of admissions from January 2013 to December 2020. These data were obtained from the Guangdong Province health statistics network direct reporting system, containing medical institutions at all levels [20, 21]. The average temperature data for the study period were downloaded through the China Meteorological Data Sharing Service System [22].
We defined COP according to the code in the 10th revision of the International Classification of Diseases of T58, which indicates a diagnosis of toxic effects of CO from all sources. If T58 was listed in any diagnosis (e.g., primary diagnosis, secondary, or tertiary) field, we included the hospital admission data in our study. Multiple admissions of the same patient were considered to be cases exposed to the same emission source. Using the encrypted ID number for each patient, we deleted duplicate entries.
Demographic data included sex, age, employment information, treatment outcome, medical payment method, and type of exposure. The employment information was classified as civil servant, professional technology personnel, business manager, worker, farmer, student, self-employed, unemployed, or other. Under the classification rules for inpatient records, the treatment outcome was classified as discharge from hospital with doctors’ orders, referral to another hospital, referral to a community health service organization, leaving against medical advice, death, or other. According to the existing medical payment methods in China, medical payments were divided into the following categories: basic medical insurance for urban employees, basic medical insurance for urban residents, new rural cooperative medical care, poverty relief, commercial medical insurance, full public expense, full fee, other social insurance, or other methods. The types of COP were classified into unintentional, intentional, and unknown types.
We considered the crude rates (CR) for hospital admission rate, expressed as the number of cases per 100,000 people. Because this study used only anonymous inpatient records and did not involve the collection, use, or transmittal of individually identifiable data, Institutional Review Board approval to conduct this study was unnecessary. We calculated age-adjusted and sex-adjusted hospital admission rates with 95% confidence intervals, using the 2020 population of China as the reference.
-
Spatial autocorrelation analysis was conducted to identify the spatial clustering of annual COP hospitalization rates in all 124 counties (including Dongguan city and Zhongshan city). The row standardized first-order contiguity Queen neighbors were used as the criterion for identifying neighbors. Moran’s I, ranging from −1 to +1, was calculated to test the spatial autocorrelation of all counties in Guangdong province. Positive/negative spatial autocorrelation occurred when Moran’s I was close to +1/−1, thus indicating that areas with similar (high-high or low-low)/dissimilar (high-low or low-high) hospital admission rates of COP clustered together. Monte Carlo randomization (9,999 permutations) was used to assess the significance of Moran’s I, with a null hypothesis that the distribution of COP in Guangdong province was completely spatially random. Subsequently, we used local indicators of spatial association (LISA; Local Moran’s I) analysis and a Moran scatter plot to examine the spatial autocorrelation of each county in Guangdong province and to determine the locations of the clusters. Moran’s plot showed high-high and low-low clustering in the upper right and lower left quadrants, respectively. Statistically significant high-high, low-low, and outlier local clusters (high-low and low-high) were visualized with a cluster map with county boundaries.
The spatially stratified heterogeneity of average COP hospitalization rates in four regions of Guangdong from 2013 to 2020 was explored. The division of these four regions in Guangdong Province (eastern Guangdong, western Guangdong, the Pearl River Delta, and northern Guangdong) was based on the official classification of the Guangdong government according to local geographical and human characteristics. Spatially stratified heterogeneity was measured with the GeoDetector q statistic [23], which indicates the level of spatially stratified heterogeneity in a range from 0 to 1, with 0 indicating random distribution and 1 indicating strong heterogeneity between strata.
-
A time series is defined as a set of data ordered by time. Through time series analysis, data ordered by time can reveal a clear description of the features of a series and present the future values of the series [24]. According to time series decomposition theory, we split the time series into three parts: trend changes, periodic changes, and random changes. The simple mathematical relationship of the model is as follows:
$$ {X}_{t}={S}_{t}\times{T}_{t}\times{I}_{t} $$ (1) here, St, Tt, and It represent seasonal information, trend information, and random fluctuation information, respectively [25]. Through decomposition of the time series, the seasonal information was collected to calculate seasonal indices, and a seasonal index distribution chart and radar chart were drawn. Meanwhile, concentration ratios (M) were calculated.
-
The nonlinear exposure-response relationship between the disease and temperature, as well as the delayed effect of that relationship, was calculated via the distributed lag nonlinear model (DLNM), in which relationships between predictors and lags were quantified on a cross-basis [26]. As in previous studies, the DLNM was used to quantify the relationships between daily temperature and the hospital admission ratio of COP [27]. The model was expressed as follows:
$$ Log\left[E\left({Y}_{t}\right)\right]=\alpha +\beta {Temp}_{t,l}+NS\left({Time}_{t},\frac{7}{year}\right)+\gamma {Dow}_{t} $$ (2) where E denotes the mathematical expectation; Yt denotes the hospital admission ratio of COP at day t; α denotes a constant term;
$\beta $ denotes the vector of temperature history in the study period; and Tempt, l denotes the matrix for quantifying the nonlinear lag effects of the mean temperatures, in which l denotes the lag time. Temperature trends were modeled by a natural spline function with a degree of freedom (df) of 7 per year. Day of the week, denoted by the variable DOW, was included to adjust for weekly patterns. Relative risk (RR) values for different temperatures were obtained on the basis of the median daily temperature. To capture the overall temperature effects, l was set to 21 days. The df was determined by Akaike’s information criterion. To assess the robustness of the model, sensitivity analyses were performed by testing different df values. -
A total of 48,854 cases of COP were recorded from January 1, 2013 to December 31, 2020 in Guangdong province, China.
As shown in Table 1, the highest CRs (10.83/105) were found in the age group of 0−14 years of age in 2018, and CRs in the age group of 0−14 years of age were higher than those in the other age groups in 2013−2020. Of these patients, 17,816 (36.47%) were male, and 31,038 (63.53%) were female. The sex ratio (male to female) was 1:1.74. The CRs and standardized rates in females were always higher than those in males in 2013–2020. As shown in Figure 1, patients between 10 and 19 years of age accounted for the largest proportion (approximately 29.42%) of patients with COP, whereas patients 20−29 years of age were the second most populous age group, at approximately 26.18%. With regard to the distribution of age and sex, we observed two peaks of inpatient number in females, at 10−14 years of age and 20−24 years of age. However, the inpatient number in males showed only one peak, in the 10–14 age group.
Category 2013 2014 2015 2016 2017 2018 2019 2020 N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)N CR (per
100,000)SR
(95%
CI)All 3,498 — — 5,212 — — 4,682 — — 9,145 — — 6,352 — — 9,224 — — 5,173 — — 5,568 — — Age (years) 0–14 610 3.91 0.70
(0.70; 0.71)835 5.06 0.91
(0.91; 0.91)901 4.78 0.86
(0.85; 0.86)1,750 9.24 1.66
(1.65; 1.66)1,393 7.25 1.3
(1.3; 1.31)2,112 10.83 1.94
(1.94; 1.95)1,252 6.68 1.2
(1.19; 1.2)1,518 6.39 1.15
(1.14; 1.15)15–64 2,752 3.34 2.3
(2.29; 2.3)4,136 5.05 3.46
(3.46; 3.47)3,592 4.47 3.06
(3.06; 3.06)6,977 8.55 5.86
(5.85; 5.86)4,674 5.64 3.87
(3.86; 3.87)6,647 7.9 5.41
(5.41; 5.42)3,614 4.2 2.88
(2.87; 2.88)3,755 4.11 2.81
(2.81; 2.82)65– 136 1.56 0.21
(0.21; 0.21)241 2.72 0.37
(0.36; 0.37)189 2.05 0.28
(0.27; 0.28)418 4.45 0.6
(0.6; 0.6)285 2.96 0.4
(0.4; 0.4)465 4.75 0.64
(0.64; 0.65)307 2.96 0.4
(0.4; 0.4)295 2.73 0.37
(0.37; 0.37)Gender Male 1,335 2.41 1.23
(1.21; 1.26)1,926 3.39 1.74
(1.71; 1.77)1,721 3.03 1.55
(1.53; 1.58)3,384 5.87 3.01
(2.97; 3.05)2303 3.93 2.01
(1.98; 2.05)3,354 5.67 2.9
(2.86; 2.94)1,851 3.07 1.57
(1.55; 1.6)1,942 2.9 1.49
(1.46; 1.52)Female 2,163 4.24 2.07
(2.03; 2.1)3,286 6.51 3.17
(3.13; 3.22)2,961 5.72 2.79
(2.75; 2.83)5,761 11 5.37
(5.31; 5.42)4,049 7.63 3.72
(3.68; 3.77)5,870 10.82 5.28
(5.22; 5.33)3,322 6.04 2.95
(2.91; 2.99)3,626 6.13 2.99
(2.95; 3.03)Note. N, number of inpatients; CR, cruel rate; SR, standard rate; 95% CI, 95% confidence interval; COP, carbon monoxide poisoning. Table 1. Demographic characteristics of COP cases in Guangdong province from 2013 to 2020
Figure 1. Distribution of carbon monoxide poisoning (COP) among inpatients by age and sex in Guangdong.
Among these COP cases, students (10,981 cases), farmers (9,524 cases), and unemployed people (8,723 cases) were the main populations among patients with COP, accounting for 22.5%, 19.5%, and 17.9%, respectively. The most used medical payment method was payment of full medical fees (approximately 37.6%). Most COP outcomes were discharge from the hospital with doctors’ orders, at 74.4%. Leaving against medical advice was the second most common outcome (11,515 cases, 23.6%). Of note, unintentional COP accounted for the majority of cases (88.1%), and intentional COP and COP due to unknown reasons accounted for 3.2% and 8.6%, respectively.
-
As shown in Figure 2, the high hospital admission rates for COP were concentrated in the northern areas of cities in Guangdong province, such as Shaoguan and Meizhou. Meanwhile, the areas with low hospital admission rates were mainly concentrated in two types of cities: well-developed cities, such as Guangzhou and Shenzhen, and cities in the south of Guangdong, such as Zhanjiang. The three counties with the highest COP annual hospital admission rates were Jiaoling (33.01/105), Wengyuan (32.57/105), and Lianshan (31.57/105). The counties with the lowest COP annual hospital admission rates were Longhua (0.02/105), Pingshan (0.03/105), and Guangming (0.09/105).
Figure 2. Annual hospital admission rates for carbon monoxide poisoning (COP) in Guangdong. GZ, Guangzhou; SG, Shaoguan; SZ, Shenzhen; ZH, Zhuhai; ST, Shantou; FS, Foshan; JM, Jiangmen; ZJ, Zhanjiang; MM, Maoming; ZQ, Zhaoqin; HZ, Huizhou; SW, Shanwei; HY, Heyuan; YJ, Yangjiang; QY, Qingyuan; DG, Dongguan; ZS, Zhongshan; CZ, Chaozhou; JY, Jieyang; YF, Yunfu.
-
The counties with high COP hospital admission rates tended to be adjacent to districts with high COP hospital admission rates, and the counties with low COP hospital admission rates tended to be adjacent to districts with low COP hospital admission rates, according to global Moran’s I values ranging from 0.23−0.53 (all P-values < 0.05). The global Moran’s I value of average annual hospital admission rates for COP was 0.447. In LISA analysis, 14 counties showed significant high-high spatial clustering, and 20 counties showed significant low-low spatial clustering in the 8-year period. The high-high spatial clustering area was mainly concentrated around the cities of Shaoguan and Qingyuan, with average annual hospital admission rates of 19.65/105 and 14.82/105, respectively. Foshan, Guangzhou, Dongguan, and Shenzhen, around the Pearl River Delta were the areas with low-low spatial clustering, with average annual hospital admission rates of 4.21/105, 2.51/105, 5.18/105, and 0.70/105, respectively (Supplementary Figure S1, available in www.besjournal.com). The overall classification of Guangdong’s COP risk from 2013 to 2020 was non-homogeneous, and the q value was 0.38 according to GeoDetector.
-
As shown in Figure 3, the trend characteristics of the time series remained stable from 2013 to 2015 and remained steady after a rapid increase in 2016. On the basis of the seasonal information and random fluctuation information of the time series indicated in the chart, the hospital admission rates for COP in Guangdong province showed significant periodicity and white noise in random fluctuation.
Figure 3. Seasonal decomposition of time series in carbon monoxide poisoning (COP) in Guangdong, China, 2013–2020.
By summarizing the number of COP cases in each year, we drew a season index distribution chart and a radar chart to visualize the monthly changes in the number of COP cases more clearly. As shown in Figure 4, the months with larger seasonal indices were January and February, followed by December; this time period is the peak period of COP. The time period from April to November had a smaller seasonal index, and displayed a trough in COP cases. A single peak was observed in the seasonal distribution for the year, which spanned December to February, with the largest number of cases in January. The concentration ratios (M) ranged from 0.73 to 0.82.
-
The patterns of four regions in Guangdong province (eastern Guangdong, western Guangdong, the Pearl River Delta, and northern Guangdong) are presented by three-dimensional plots of the RR according to mean temperature (Figure 5). The RRs versus mean temperature effects with a lag of 3 days are shown in Figure 6. Low temperature was associated with high risk of COP, with a lag lasting 7 days.
Figure 5. The 3D plot of RRs for daily carbon monoxide poisoning (COP) cases under different temperatures and lag combinations in four regions. The horizontal axis coordinate represents temperature and lag range, and the vertical axis coordinate represents RRs, which were estimated with DLNM by using the daily temperature median as a reference (RR = 1).
Figure 6. The relative risks of carbon monoxide poisoning (COP) according to daily temperatures at a lag of 3 days and temperatures in four regions. The curve denotes the maximum likelihood estimate of RRs, and the 95% CIs are shown in the gray regions.
The estimated effects of mean temperature on the hospital admission rates for COP were nonlinear, with higher relative risks at colder temperatures. With a lag of 0 days, the effects of low temperatures [extreme minimum (2 °C) and 5th (12 °C)] on COP were 5.98 (95% CI: 4.91–7.30) and 3.26 (95% CI: 3.06–3.47), respectively, as compared with the reference temperature [median (24 °C)] in eastern Guangdong. Similarly, in western Guangdong, the Pearl River Delta and northern Guangdong, the effects of low temperatures [extreme minimum (2 °C)] on COP were 7.26 (95% CI: 6.14–8.57), 3.83 (95% CI: 3.57–4.11), and 4.37 (95% CI: 4.06–4.69), respectively. The effects of low temperatures [5th (12 °C)] on COP were 3.81 (95% CI: 3.61–4.01), 2.4 (95% CI: 2.33–2.48), and 2.24 (95% CI: 2.15–2.34), respectively.
With a lag of 3 days, the effects of low temperatures [extreme minimum (2 °C) and 5th (12 °C)] on COP were 2.06 (95% CI: 1.89–2.24) and 1.7 (95% CI: 1.66–1.74), respectively, in eastern Guangdong, compared with reference temperature. Similarly, in western Guangdong, the Pearl River Delta, and northern Guangdong, the effects of low temperatures [extreme minimum (2 °C)] on COP were 2.36 (95% CI: 2.19–2.54), 2.09 (95% CI: 2.03–2.15), and 1.74 (95% CI: 1.7–1.79), respectively. The effects of low temperatures [5th (12 °C)] on COP were 1.66 (95% CI: 1.63–1.69), 1.49 (95% CI: 1.48–1.51), and 1.40 (95% CI: 1.38–1.42), respectively.
With a lag of 7 days, the effects of low temperatures [extreme minimum (2 °C) and 5th (12 °C)] on COP were 1.01 (95% CI: 0.91–1.11) and 1.11 (95% CI: 1.08–1.13), respectively, in eastern Guangdong as compared with the reference temperature. Similarly, in western Guangdong, the Pearl River Delta, and northern Guangdong, the effects of low temperatures (extreme minimum [2 °C]) on COP were 1.11 (95% CI: 1.02–1.21), 1.22 (95% CI: 1.18–1.26), and 1.07 (95% CI: 1.04–1.11), respectively. The effects of low temperatures (5th [12 °C]) on COP were 1.04 (95% CI: 1.02–1.06), 1.1 (95% CI: 1.09–1.11), and 1.05 (95% CI: 1.04–1.07), respectively.
HTML
Study Area
Data Collection
Spatial Analysis
Temporal Trend Analysis
Distributed Lag Nonlinear Model
Demographic Characteristics of CO Poisoned Patients
Spatial Distribution Characteristics of COP
Spatial Analysis of COP Hospital Admission Rates
Time Distribution Characteristic Analysis of COP
Association between Temperatures and COP
22022Supplementary Materials.pdf |