Effects of Ambient Temperature on Bacillary Dysentery: a Multi-City Analysis in Anhui Province, China

Science of the Total Environment 671 (2019) 1206–1213

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Science of the Total Environment

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Effects of ambient temperature on bacillary dysentery: A multi-city analysis in Anhui Province, China

Yanbin Hao a,e,1, Wenmin Liao a,1,WanwanMab,1, Jin Zhang b,NaZhanga,ShuangZhongc,ZheWangd,⁎, Lianping Yang a,⁎⁎,CunruiHuanga a Department of Health Policy and Management, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China b Anhui Provincial Center for Disease Control and Prevention, Hefei 230601, Anhui, China c Center for Chinese Public Administration Research, School of Government, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China d Chinese Center for Disease Control and Prevention, 102206 Beijing, China e Department of Preventive Medicine, Gannan Medical University, Ganzhou 341000, Jiangxi, China

HIGHLIGHTS GRAPHICAL ABSTRACT

• High weekly mean temperature increased risks of bacillary dysentery (BD) in Anhui. • Urbanization level modiﬁed associations between high temperature and BD incidence. • For children b5, effect of high temperature on BD increased in high urbanized cities.

article info abstract

Article history: Background: Rising ambient temperature is expected to increase incidence of bacillary dysentery (BD), but few Received 10 December 2018 studies have compared the temperature-BD effects of different age groups and cities in China, especially in a Received in revised form 18 March 2019 multi-city setting. Accepted 28 March 2019 Objectives: We used city-specific data including BD cases and meteorological variables to determine the relation- Available online 29 March 2019 ship between BD incidence and temperature at provincial level. Methods: Weekly BD disease surveillance data and meteorological variables were collected in all 16 Editor: Scott Sheridan prefecture-level cities in Anhui Province of China. Firstly, city-specific weekly mean temperature-BD inci- fi Keywords: dence associations were estimated with Distributed Lag Nonlinear Model (DLNM). Secondly, city-speci c Temperature estimates were pooled at province-level through multivariate meta-analysis. Also, we conducted subgroup Bacillary dysentery analyses for ages (children b5 years old and population of other ages) and urbanization of cities (high and Urbanization low level), respectively. Children Results: In Anhui, BD morbidity risk increased with increasing weekly mean temperature. Relative risks (RR) at China the 90th percentile (27.5 °C) versus the 50th percentile (17 °C) of weekly mean temperature were 1.42 (95% confidence interval (CI): 1.16, 1.75) and 2.02 (95% CI: 1.76, 2.32) for children b5 and population of other ages, respectively. The relative risk of high temperature on other ages group was higher than that of children under

⁎ Correspondence to: Z. Wang, Chinese Center for Disease Control and Prevention, Beijing 102206, China. ⁎⁎ Correspondence to: L. Yang, Department of Health Policy and Management, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong, China. E-mail addresses: [email protected] (Z. Wang), [email protected] (L. Yang). 1 These authors contributed equally to this work.

ﬁve years old (p = 0.006). Children under 5 in high urbanized cities appeared to be more vulnerable to the effects of ambient high temperature (RR: 1.56, 95% CI: 1.20, 1.92) than in low urbanized cities (RR: 1.01, 95% CI: 0.70, 1.46), the difference between two intervals was statistically signiﬁcant (p =0.044). Conclusions: This study suggests that high temperatures may be an important trigger of BD incidence, and especially lead to a substantial burden of BD for high urbanized cities in Anhui Province of China. © 2019 Published by Elsevier B.V.

1. Introduction 2. Methods

Bacillary dysentery (BD) is an intestinal infectious disease caused by 2.1. Study location the bacterial pathogen Shigella spp., and its clinical symptoms mainly in- clude fever, bloody diarrhea, abdominal cramps and tenesmus (Kelly- Anhui Province is located in eastern China, including 16 prefecture- Hope et al., 2008; Lee et al., 2017). It is spread by fecal contamination level cities (Fig. 1), with moderate economic development and a high of food or water or by person-to-person contact (Kelly-Hope et al., proportion of rural population (61.3%) (Gao et al., 2016). The study 2008; Lee et al., 2017). It is estimated that there are more than 80 mil- area locates in a sub-humid warm temperate continental monsoon cli- lion cases of bloody diarrhea and 700,000 related deaths worldwide mate zone, with a hot and rainy weather in summer seasons (Gao each year, of which about 99% occur in developing countries (Lee et al., 2016). We collected all 16 cities shown in Fig. 1 as the basis of et al., 2017). our analyses. Since 2004, the Chinese center for disease control and prevention has reported infectious diarrhea disease including BD as a Class B infec- 2.2. Classification of cities by urbanization tious disease through China Information System for Disease Control and Prevention (CISDCP). Although the Chinese government has been com- The quartile classification of 16 cities according to urbanization rate mitted to preventing and controlling BD, the incidence of BD in China is was shown in Fig. 1. The urbanization rate of a city refers to the percent- also gradually decreasing, but the disease burden remains high (Zhang age of the population living in urban areas in the total population. Ac- et al., 2016). Update to study period of 2015, annual incidence rate of cording to the 2016 Anhui Province statistical yearbook (Statistics BD was approximately 16.1 per 100,000 in Anhui Province and 10.1 Bureau of Anhui Province and NBS Survey Office in Anhui, 2016), the per 100,000 in China. The BD burden of children under 14 years of age 25th percentile (P25) of the urbanization rate was 45.1%, and the 75th had exceeded that of other age groups (Li et al., 2016). In Chinese stud- percentile (P75) of the urbanization rate was 60.7%. ies, BD cases occurred most often in the summer and autumn, roughly from May to September (Yan et al., 2017; Zhang et al., 2016), and ob- 2.3. Disease data served north-south differences in peak duration (Zhang et al., 2016), which seems to be driven by both human and environmental factors, in- BD is a class B national infectious disease in China. According to diag- cluding socio-economic, health conditions and climate change. Specifi- nostic criteria for BD (WS287–2008) in China (Ministry of Health of the cally, in the process of disease transmission and spatial and temporal People's Republic of China, 2008), a clinically diagnosed BD case was de- distribution, climatic factors possibly played an important role (Kelly- fined as a patient with the following clinical features: fever, chills, ab- Hope et al., 2008). dominal pain, tenesmus, bloody or mucus stool or stool containing Climate change will be an important factor affecting the health of N15/high power field (HPF) leukocytes or purulent cells, and microscop- people in vulnerable areas (Kolstad and Johansson, 2011). Temperature ically discernible red blood cells and phagocytic cells. A confirmed BD is considered to be the primary factor affecting the incidence of infec- case was defined as a patient through clinical diagnosis combined tious diarrhea. Some studies examined the relationship between tem- with the pathogenic examination of isolation of Shigella spp. from a perature and BD in single cities. Such as in Jinan, a 1 °C increase in stool specimen (Chang et al., 2016). All clinical and hospital doctors monthly maximum temperature might relate to an 11.40% increase in must report all clinically diagnosed and confirmed BD cases to the BD (Zhang et al., 2008). In Changsha, a 1 °C rise in mean temperature, local centers for disease control and prevention (CDC) within 24 h mean maximum temperature, and mean minimum temperature through the internet-based Chinese disease control and prevention in- might lead to 14.8%, 12.9%, and 15.5% increases in the incidence of BD formation system. In this study, BD cases included clinically diagnosed disease, respectively (Gao et al., 2014). And in Beijing linear relationship cases, and confirmed cases through clinical diagnosis combined with was observed between temperature and BD for temperature N12.5 °C the pathogenic examination. (Li et al., 2015). The relative risk in Hefei associated with a 1 °C increase Daily BD records from 1 January 2010 to 31 December 2015 were in temperature was 1.04 (95% CI 1.00–1.07) (Cheng et al., 2017). Evi- collected by Anhui provincial CDC. Case information included gender, dence suggests that the incidence of BD in some Chinese cities increases age, residential address, and date of onset. All extracted BD cases with as temperatures rise, but different local climatic conditions, different complete and accurate geographical location were classified and sum- data scales and analysis methods lead to different conclusions (Yan marized according to their respective cities. et al., 2017). As climate change continues, climate-related burden of diarrhea 2.4. Meteorological data may increase (Levy et al., 2016), and it is of great significance to quantify the relationship between meteorological factors and infectious diarrhea City-specific daily mean temperature (°C), maximum temperature in China, and yet there are very few large-scale examples of these. Few (°C), minimum temperature (°C), average relative humidity (%), and ac- studies have investigated the associations between high temperature cumulated rainfall (mm) were obtained from the China Meteorological and BD in China, especially in a multi-city setting. We used these city- Data Sharing Service System (http://data.cma.cn/). The information of specific data including BD cases and meteorological variables to deter- matching weather stations was shown in Fig. 1 and other literatures mine the relationship between BD incidence and high temperature at (Gao et al., 2016). We used meteorological data from another city provincial level, and examine high temperature associated with high- with close distance and latitude to fill in missing meteorological data risk ages and cities. from Tongling and Chizhou weather stations (only from January 2010 1208 Y. Hao et al. / Science of the Total Environment 671 (2019) 1206–1213

Fig. 1. Map of Anhui Province in China (panel A), and showing the location, urbanization rate and weather station in each prefecture-level city (panel B). There were altogether 16 prefecture-level cities in Anhui. They were Hefei, Wuhu, Bengbu, Huainan, Ma'anshan, Huaibei, Tongling, Anqing, Huangshan, Chuzhou, Fuyang, Suzhou, Lu'an, Bozhou, Chizhou and Xuancheng. Different cities were distinguished by color according to quartiles of the rate of urbanization. The location of weather stations was marked with “▲”. to April 2012, Tongling by Wuhu, Chizhou by Anqing). Daily meteoro- week time (df = 5). To control the auto-correlation, we included the logical data were averaged over a week to obtain weekly meteorological first-order lagged residual item into models. data. We combined city-specific estimates through fixed-effects meta- analysis. The estimates of multivariate meta-analysis represented the 2.5. Data analysis province-level effects were obtained through an intercept only model. We predicted the average relative risk (RR) with confidence interval We used two-stage analytic approach. Firstly, city-specific weekly (CI) of exposure-response relationship, over average mean temperature mean temperature-BD incidence associations were estimated with Dis- and temperature range across cities (Gasparrini et al., 2012). Cochran's tributed Lag Nonlinear Model (DLNM). Secondly, city-specificestimates Q-test and I2 statistic was applied to evaluate heterogeneity across cit- were pooled at province-level through multivariate meta-analysis ies. Cochran's Q-test could test the significance of heterogeneity. Multi- (Gasparrini et al., 2015). variate meta-regression models, each including a single meta-predictor A generalized linear model combined with a DLNM with a quasi- (urbanization rate and per capita GDP), were used to extend the analy- Poisson distribution to account for over-dispersion was applied sepa- sis by Wald test (Gasparrini et al., 2012). To explore the explained per- rately in each city in order to obtain estimates of city-specific centage of city-specific factors on heterogeneity, we computed the temperature-BD associations. DLNM could describe complex nonlinear spatial stratified heterogeneity q-statistic (SSH-q)(Wang et al., 2016). and lagged dependencies of temperature-BD through a cross-basis We selected the city-specific RRs at 27.5 °C as dependent variable, and function, which defined the conventional exposure-response relation- urbanization rate was categorized into three levels (bP25, P25-P75, ship and the additional lag-response relationship, respectively NP75). Factor detector results showed q statistic, and the percentage of (Gasparrini, 2014). We selected a cross-basis composed of one ns (nat- heterogeneity explained by urbanization rate equaled 100q%(Wang ural cubic B-spline, df = 3) for the exposure-response with two knots et al., 2016). placed at the 33th and 67th percentiles of city-specific temperature dis- Using meta-regression model, the exposure–response associations tributions, and another ns (df = 3) for the lag-response with an inter- were predicted for the values of the 25th percentile (low level) and cept and one internal knot placed at 2 for lag of 0–4 weeks to capture 75th percentile (high level) of urbanization rates (Gasparrini et al., the delayed effects of extreme temperature. For exposure–response re- 2012). We summarized the results by computing the RRs at the 90th lationship, ns with 3 df was fixed based the assumption that both high percentile (27.5 °C) from these curves, using the 50th percentile (P50) and low temperature may have impact on BD incidence. The lag period of weekly mean temperature (17 °C) as the reference. We compared (0–4 weeks) was expected to capture the effects of meteorological var- those overlapped 95% CIs of the RRs of different ages and urbanization iables on intestinal infectious diseases with an average lag period of subgroups using the method introduced by Altman and Bland (Altman 3–4 weeks even 1 month (Singh et al., 2001; Zhang et al., 2008). and Bland, 2003). In this method, z test was used to test the difference Based Quasi Akaike information criterion (QAIC), relative humidity between log transformed relative risks. and rainfall were controlled for by using ns (df = 2 and 2), respectively. We conducted subgroup analyses for populations of different ages To control for seasonal pattern and long-term trend, we used the ns of (children b5 years of age and population of other ages) and cities of Y. Hao et al. / Science of the Total Environment 671 (2019) 1206–1213 1209

Table 1 Table 2 Descriptive statistics for weekly meteorological variables and bacillary dysentery cases Meta-pooled cumulative effects: high temperature-BD in Anhui of China. among 16 cities of Anhui, China, 2010–2015. Population Cities Weekly mean temperature Variables Mean Min P25 Median P75 Max (P90/27.5 °C vs. P50/17 °C)

Bacillary dysentery (Total) 14.3 0.0 2.0 6.0 15.0 259.0 RR 95% CI Children b5 3.9 0.0 0.0 1.0 4.0 80.0 Children b5 Total 1.42 1.16–1.75 Other ages 10.4 0.0 2.0 4.0 11.0 210.0 High urbanized 1.56 1.20–1.92 Mean temperature (°C) 15.9 −4.2 7.2 17.3 23.9 34.4 Low urbanized 1.01 0.70–1.46 Min temperature (°C) 12.1 −9.2 3.5 12.9 20.6 30.1 Other ages Total 2.02 1.76–2.32 Max temperature (°C) 20.9 0.7 12.9 22.8 28.4 41.0 High urbanized 2.16 1.84–2.53 Relative humidity (%) 71.8 25.5 64.7 73.3 80.3 97.4 Low urbanized 1.67 1.32–2.13 Rainfall (mm) 3.2 0.0 0.1 1.3 4.1 54.9

ages. Summarized by RRs at the 90th percentile versus the reference different urbanization level (high and low), respectively. We performed of 1.42 (95% CI: 1.16, 1.75) and 2.02 (95% CI: 1.76, 2.32), for children sensitivity analysis by changing df of ns for weather variables and time, b5 and population of other ages, respectively. The relative risk of high and conducted the Partial autocorrelation function (PACF) plots of the temperature on other age groups was higher than that of people residuals of models to check whether existed autocorrelation (Supple- under five years old (p =0.006). mentary figures). All analyses were performed using ArcGIS 10.5 The province-pooled specific lag-response curves at the 90th per- (ESRI, USA) and R software (package “dlnm” and “mvmeta”). centile (P90) of weekly mean temperature percentile (27.5 °C) are represented in Fig. 3 (panel B/D). For two age groups, the lag effects seemed 3. Results increased after lagged two weeks, and most of the risk was limited to 4 weeks (lag 0–4). Summary statistics for weekly numbers of BD cases reported and The Cochran Q test provided evidence for heterogeneity for two age meteorological variables in 16 cities of Anhui during Jan 1st 2010 - groups (Table 3). And through univariate meta-regression analysis and Dec 31st 2015 were shown in Table 1. There was a total of 72,649 BD Wald test, we found urbanization rate was a significant meta variable cases during the study period in Anhui, with 19,959 cases of children for both two age groups (p = 0.000). I2 statistics dropped from 34.5% under 5 years of age. The most prevalent cities were Fuyang (19,678), to 18.7% for children under 5, and from 37.9% to 22.3% for population Suzhou (14,960), and Hefei (11,242). Wuhu (0.4665) had the highest of other ages. Results of spatial stratified heterogeneity q-statistic proportion of childhood BD cases, followed by Hefei (0.3299), and showed that urbanization rate explained 11.4% (p = 0.515), 13.3% (p Lu'an (0.3183). In Anhui, the BD incidence peaked in 26th–33rd weeks = 0.442) of heterogeneity for under five and other ages, respectively. (July–August) according with high ambient temperature, as shown in Fig. 4 showed the exposure-response and lag-response relationships Fig. 2. From 2013 onward, the number of BD cases reported at the of subgroup analyses by age and urbanized level of cities (values see in peak in Anhui decreased approximately by half. Table 2). For children under five, Fig. 4 (panel A) illustrated that high The associations between weekly mean temperature and BD inci- temperatures, on average, had a greater impact in high urbanized cities, dence estimated from model were illustrated in Table 2 (for all cities) with a significant raise in risk: the RR at 27.5 °C versus 17 °C increased and Fig. 3 (panel A/C). As a whole for all study cities of Anhui, both from 1.01 (95% CI: 0.70, 1.46) for low urbanized cities to 1.56 (95% CI: the 50th percentile (P50) of weekly mean temperature (17 °C) was 1.20, 1.92) for high urbanized cities (p = 0.044). For other ages group the reference for two age groups: children b5 and population of other in Fig. 4 (panel C), the RR changed from 1.67 (95% CI: 1.32, 2.13) for low urbanized cities to 2.16 (95% CI: 1.84, 2.53) for high urbanized cities (p = 0.082).

4. Discussion

This study examined the impacts of high temperature on BD disease in a multi-city setting within the province of Anhui of China using the DLNM model, and conﬁrmed that the positive associations between weekly BD incidence and high temperature in Anhui Province had var- ied over cities and ages. Most cases occurred in 26th–33rd week (July and August), which is consistent with the increasing BD cases associated with rising temperature. Our ﬁndings suggest that public health pro- grams should focus on preventing BD related to high temperature, especially among high urbanized cities in Anhui of China. In this study the effects of high temperature on BD incidence were estimated using DLNM model. We applied the DLNM model to reveal changes in the risk of BD incidence with ambient temperature, including the cumulative effects of different temperatures over the coming four weeks (Lag 0–4 weeks) in Fig. 3 (panel A/C), and the effect of high temperature conditions (27.5 °C) on subsequent BD incidence at different lag weeks (Lag = 0,1,2,3,4, respectively) in Fig. 3 (panel B/D). Some studies also had assessed the associations of high ambient temperature and different kinds of diarrhea, such as non-cholera diarrhea (Hashizume et al., 2007), infectious gastroenteritis, and bacillary dysentery (Zhang et al., 2007). Studies in some Chinese cities had found that increased temperature would lead to more cases of BD, such as in Fig. 2. Weekly numbers of cases for bacillary dysentery and mean temperature in Anhui of Jinan (Zhang et al., 2008), Changsha (Gao et al., 2014), Beijing (Li China 2010–2015. et al., 2015) and Hefei (Cheng et al., 2017). 1210 Y. Hao et al. / Science of the Total Environment 671 (2019) 1206–1213

Fig. 3. Meta-pooled and city-speciﬁc cumulative and lag effects between weekly mean temperature and BD incidence for Children under 5 (panel A/B) and other ages population (panel C/D).

Shigella spp. are bacteria that causes BD. Shigella is suitable for conditions, people like cold drinks and cold foods will also increase growth at temperatures ranging from 6–8°Cto45–47 °C and an opti- the outbreak of food-borne BD disease. mum temperature of 37 °C (ICMSF, 1996). Higher temperature might Our study found that the relationship between the risk of BD inci- increase the survival rate and replication rate of pathogens in the envi- dence and temperature above a certain threshold was almost linear ronment. Shigella can be transmitted through the fecal-oral route or consistently with other study (Liu et al., 2019). Studies in Hefei (the cap- through person-to-person contact or by consumption of contaminated ital city of Anhui Province) revealed that temperature effects elevated food or water. A study of outbreaks of food-borne BD in the United linearly above 18.4 °C (temperature threshold in warm season) States had shown about 50% of outbreaks were associated with con- (Cheng et al., 2017), specially the temperature threshold was 17.0 °C sumption of raw food (Nygren et al., 2013). Also, the survival rate of Shi- for under-ﬁve children BD (Li et al., 2016). Also, in Beijing city, with gella is usually increased when the food is kept at a refrigerated an approximately linear effect for temperature N12.5 °C (Li et al., temperature (4 °C) (Warren et al., 2006). Under high temperature 2015), and in Shanghai city between 12 °C and 22 °C, temperature was approximately positively linear correlated with the logarithm of BD counts (Ma et al., 2013). No thresholds were detected in the southern city of China (Zhang et al., 2007). Consistently with other studies, Table 3 temperature and BD generally show a linear relationship within a cer- Tests of heterogeneity for second-stage multivariate meta analyses. tain temperature range. Of course, the majority of BD cases tend to Population Meta models Cochran Q test I2 Wald test occur at mild high temperatures (Cheng et al., 2017). In Fig. 3 (panel Qdfp% stat p C), the cumulative effects curve of Xuancheng city was different from other cities. In Xuancheng for other ages group (above 5 years old), Children b5 Intercept only 206.1 135 0.000 34.5 –– Urbanization rate 155.0 126 0.040 18.7 51.0 0.000 the proportion of cases that occurred when mean temperature was Per capita GDP 197.9 126 0.000 36.3 8.2 0.515 below 17 °C reached 35% (293/827), which was higher than the average Other ages Intercept only 217.4 135 0.000 37.9 –– ratio of other cities (24%). This result may reveal that not only high tem- Urbanization rate 162.2 126 0.016 22.3 55.2 0.000 peratures were associated with the onset of BD, but also low tempera- Per capita GDP 194.2 126 0.000 35.1 11.6 0.233 tures would have an impact on BD incidence in the speciﬁccity. Y. Hao et al. / Science of the Total Environment 671 (2019) 1206–1213 1211

Fig. 4. Meta-pooled cumulative and lag effects between weekly mean temperature and BD mobility for Children under 5 (panel A/B) and other ages population (panelC/D)fromhighand low urbanized cities.

In our study most of the effects of high temperature on BD were lim- the impact of rainfall and humidity on the transmission of BD has not ited to 4 weeks (lag 0–4). This result was consistent with other studies been consistently reported, including positive (Li et al., 2015; on meteorological variables and intestinal infectious diseases, which McCormick et al., 2012), negative (Li et al., 2013) and uncorrelated re- found an average lag effect of 3–4 weeks or 1 month (Singh et al., sults (Zhou et al., 2013). Meanwhile, the mechanism of the association 2001; Zhang et al., 2008). The temperature in the previous 1 month between meteorological variables and BD is still not clear, because was most correlated with the number of monthly BD cases (Gao et al., there are few studies on the quantitative relationship between meteo- 2014). A four-week lag was biologically possible because that time in- rological factors and BD worldwide (Lee et al., 2017). cludes bacteria that grow in the best environment, spread through con- To examine high temperature associated with high-risk ages and cit- taminated food and water, from the onset of an intestinal infection (Gao ies, we further conducted stratified analyses by age and city. Our study et al., 2014; Zhang et al., 2008). Our study also showed that the effects of found that children under 5 living in high urbanized cities than those extreme heat over 2 weeks on the incidence of BD were still present, es- in low urbanized cities appeared to be more vulnerable to the effects pecially for children under 5 years of age. We thought both re-infected of ambient high temperature. Young children have relatively undevel- cases and lack of effective treatment and vaccines were responsible for oped immune systems and lower self-care abilities, which means that the long-term lag effects. If a child had acute bacterial dysentery, who they are unable to protect themselves against an unsafe environment did not receive standardized antibiotics treatment or still didn't pay at- and may therefore be more vulnerable to temperature changes (Wen tention to hygiene, it was very easy for children to be re-infected in the et al., 2016). short term (Khalili, 2014). This study indicated that the long-term ef- Regional differences in socioeconomic status also play an important fects of meteorological factors on BD also deserve attention. role in the spread of infectious diseases. As the largest developing coun- Temperature is one of the main meteorological factors leading to BD try in the world, China is experiencing rapid urbanization. Urban sprawl, propagation. Besides temperature, climate factors such as rainfall, rela- characterized by “heat island effect” and high population density, is tive humidity may affect the replication and/or survival of some bacte- challenging public health systems. Previous studies have shown that rial, viral and protozoan pathogens (Kolstad and Johansson, 2011). But urban areas are warmer than rural areas because of the urban heat 1212 Y. Hao et al. / Science of the Total Environment 671 (2019) 1206–1213 island effect (Cheng et al., 2017). Also, central districts of the city of Sources of financial support Wuhan were found to be the high-risk areas of BD incidence (Li et al., 2013). And, population density can modify the relationship between This study was supported by grants from the National Key Research temperature and BD, with an increase of 1000 people per square kilo- and Development Program of China (2018YFA0606200) and Asia-Pa- meter associated with the increase of BD being 240% (Xiao et al., cific Network for Global Change Research (CRRP2016-10MY-Huang). 2014). Dense urban populations promote transmission through direct Data are not available in open access. Source code of analyses is and indirect human contact (Zhang et al., 2016). BD epidemics are available from corresponding author by request. most common in crowded areas with inadequate sanitation (Gao et al., 2014). In china, the extent of the impact of climate factors may Appendix A. Supplementary data be highly dependent on sanitation infrastructure in different regions (Kolstad and Johansson, 2011). So, the development of BD prevention Supplementary data to this article can be found online at https://doi. and control in urban villages, which provide humble shelter for mi- org/10.1016/j.scitotenv.2019.03.443. grants and local poor from rural to urban areas, should be intensified (Zhang et al., 2016). This study can lead to increased public awareness and plans for better interventions during the high-temperature season References to prevent or reduce the potential outbreak or spread of BD. Further re- fi Altman, D.G., Bland, J.M., 2003. Interaction revisited: the difference between two esti- search on speci c transmission pathways of BD (water or food trans- mates. BMJ. 326, 219. mission) is necessary in highly urbanized cities. Chang, Z., Zhang, J., Ran, L., Sun, J., Liu, F., Luo, L., et al., 2016. 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Hyg. 90 (4), should actively take measures to prevent and control infectious dis- 697–704. eases, such as investing in health facilities, strengthening the manage- Gao, L., Zhang, Y., Ding, G., Liu, Q., Jiang, B., 2016. Identifying flood-related infectious dis- ment of safe drinking water and developing vaccines, which will eases in Anhui Province, China: A spatial and temporal analysis. Am. J. Trop. Med. Hyg. 94 (4), 741–749. significantly reduce the burden of diseases related to diarrhea caused Gasparrini, A., 2014. Modeling exposure–lag–response associations with distributed lag by global warming, including BD (Kolstad and Johansson, 2011). There- non-linear models. Stat. Med. 33 (5), 881–899. fore, under the premise of considering regional climatic conditions and Gasparrini, A., Armstrong, B., Kenward, M.G., 2012. Multivariate meta-analysis for nonlinear and other multi-parameter associations. Stat. 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