- Research article
- Open Access
- Open Peer Review
This article has Open Peer Review reports available.
Spatial–temporal pattern and risk factor analysis of bacillary dysentery in the Beijing–Tianjin–Tangshan urban region of China
© Xiao et al.; licensee BioMed Central Ltd. 2014
Received: 9 May 2014
Accepted: 22 September 2014
Published: 25 September 2014
Bacillary dysentery remains a major public health concern in China. The Beijing–Tianjin–Tangshan urban region is one of the most heavily infected areas in the country. This study aimed to analyze epidemiological features of bacillary dysentery, detect spatial-temporal clusters of the disease, and analyze risk factors that may affect bacillary dysentery incidence in the region.
Bacillary dysentery case data from January 2011 to December 2011 in Beijing–Tianjin–Tangshan were used in this study. The epidemiological features of cases were characterized, then scan statistics were performed to detect spatial temporal clusters of bacillary dysentery. A spatial panel model was used to identify potential risk factors.
There were a total of 28,765 cases of bacillary dysentery in 2011. The results of the analysis indicated that compared with other age groups, the highest incidence (473.75/105) occurred in individuals <5 years of age. The incidence in males (530.57/105) was higher compared with females (409.06/105). On a temporal basis, incidence increased rapidly starting in April. Peak incidence occurred in August (571.10/105). Analysis of the spatial distribution model revealed that factors such as population density, temperature, precipitation, and sunshine hours were positively associated with incidence rate. Per capita gross domestic product was negatively associated with disease incidence.
Meteorological and socio-economic factors have affected the transmission of bacillary dysentery in the urban Beijing–Tianjin–Tangshan region of China. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.
Bacillary dysentery remains a major public health concern in China . The disease is a severe form of shigellosis and is caused by infection with Shigella bacteria . The bacteria are primarily transmitted via the fecal-oral route . The major symptoms of bacillary dysentery are acute diarrheal episodes, with at least one of the following: fever, abdominal pain, tenesmus, tenderness in the left lower quadrant, and bloody or mucus stool . Worldwide, there are 165 million cases of bacillary dysentery, and 1.1 million cases of death caused by bacillary dysentery every year . In China, approximately 269,299 bacillary dysentery cases were reported in 2009, with an incidence rate of 20.28 per 100,000 [6, 7]. Bacillary dysentery is the third leading notifiable disease in China, following tuberculosis and hepatitis B .
The results of many studies have indicated that climate variations have an important part in transmission of the disease, and more research has recently been focused on this issue [7, 8]. The replication and survival of the pathogens in the environment are directly affected by temperature . Precipitation can contaminate drinking water, especially in rural areas with poor water supplies and sanitation infrastructure . Weather conditions can also affect daily lifestyle habits. For example, individuals are less likely to go outdoors during windy environmental conditions. As well as meteorological factors, socio-economic factors are relevant to the epidemiology of bacillary dysentery. For example, the transmission of the disease increases in overcrowded environments with poor sanitation . Several studies have examined the effects of climate on bacillary dysentery. However, to our knowledge no studies have been published that examine the effect of climate in combination with socio-economic factors in China.
The Beijing–Tianjin–Tangshan urban region is one of the three major urban agglomerations in China, and encompasses an area of 43,107.54 km2. It has a population of 41.87 million, located in a temperate monsoon climate zone with high climatic variation. In recent years, the incidence of bacillary dysentery has been significant higher in this region compared with other areas. An exploration of the spatial-temporal pattern and factors that affect the incidence of bacillary dysentery would aid in the identification of high-risk areas, and thus guide appropriate allocation of public health resources for better disease control and prevention.
The scan statistics
Two scan statistics that provided complementary information were used for the analysis. The purely spatial scan statistic was used to determine the geographical area with the highest risk. The space-time permutation scan statistic was used to find space-time outbreaks that are adjusted for, and are therefore not the result of purely temporal or purely spatial variation.
(a) Purely spatial scan statistic
where n z was the number of cases inside a window, n G was the total number of cases, and μ z was the expected number of cases inside the window. The LLR value was ranked in decreasing order and the largest LLR value was defined as the most likely cluster. The p-value for the scan statistic was calculated using Monte Carlo hypothesis testing.
(b) Space-time permutation scan statistic
where c A and μ A were the observed and expected number of cases in the cylinder, respectively. C was the total number of observed cases. A p-value for the scan statistic was calculated using Monte Carlo hypothesis testing.
Spatial panel model
where i and t were index spatial and time dimensions, respectively; y it was the dependent variable at spatial unit, i, and time, t; x it was the observation for the independent variable at i and t; β was the spatial regression coefficient explaining the relationship between the independent and dependent variables; ϵ it was the error term with zero mean and equal variance and was assumed to have a normal distribution; and μ i represented the spatial specific effects in different spatial units. ρ was the spatial autoregressive coefficient reflecting the spatial neighborhood effects. For ρ∈ [0,1], a high value indicated strong spatial autocorrelation, and a low value indicated weak spatial autocorrelation. If ρ = 0, then the spatial panel model degenerated to a traditional pane model. w ij was a spatial weights matrix, which indicated the spatial neighborhood relationship between regions in the dataset . For example, in the order of one neighborhood matrix, if region i was directly adjacent to region j, w ij = 1, and if not, w ij = 0.
Results for spatial cluster analysis of bacillary dysentery
Number of counties
(116.17° E, 39.93° N)
(117.19° E, 39.12° N)
(117.14° E, 40.21° N)
(117.05° E, 39.74° N)
Results for spatial-temporal outbreak detection of bacillary dysentery
Number of counties
(116.96° E, 38.86° N)
(116.21° E, 40.22° N)
(118.36° E, 40.23° N)
(116.41° E, 39.65° N)
Disease incidence was a dependent variable in a spatial panel model. Four independent variables were examined for the January 2011 to December 2011 period. They including four meteorological factors (i.e., monthly average temperature, monthly average relative humidity, monthly cumulative rainfall, and monthly total sunshine). The splm package in the R 3.02 statistical software program was used for the spatial panel modeling .
Results of spatial panel model using meteorological risk factors
Average temperature (°C)
Sunshine hours (hour)
Socio-economic risk factors also contributed to the spatial temporal distribution of the disease. However it was difficult to obtain data on the monthly variation in these factors, so spatial panel modeling could not be used for the analysis. A spatial lag model (SLM) was used for analysis of these factors. Disease incidence was a dependent variable and the two socio-economic factors (including population density and GDP per capita) were independent variables. The SLM was implemented using GeoDa software (GeoDa Center, Tempe, AZ, USA).
Results of spatial lag model using socio-economic risk factors
GDP per capita(1000 Yuan)
Population density (1000 person/km2)
Bacillary dysentery remains a major public health concern in China. The Beijing–Tianjin–Tangshan region is the largest urban agglomeration in north China, In recent years, it has experienced a notably high incidence of bacillary dysentery compared with other areas. This study explored the epidemiologic characteristics of the disease, and detected the high-risk areas using scan statistics. The associations between bacillary dysentery and meteorological variables, as well as socio-economic factors, were examined. The results indicated that (1) the incidence of bacillary dysentery in the region was still high, especially in children; (2) the risk area was mainly located at the areas with high population densities and disease outbreaks were mainly distributed in suburban district areas during festivals and holidays; (3) meteorological factors have significantly affected the transmission of dysentery. Population density has also had a significant influence.
The exploration of the epidemiologic characteristics revealed that the incidence of bacterial dysentery changed with the seasons during the 1-year study period. It was higher during summer and fall, with the peak appearing in August. The incidence in children was much higher compared with other age groups. These findings are similar to the findings of previous studies conducted in other regions of China . The seasonal variation in incidence may be associated with meteorological risk factors, which will be discussed below. The difference in incidence between males and females <5 years of age may be because boys are more active than girls, and thus would have more opportunities to be exposed to environments containing bacteria.
The transmission of Shigella can be affected by many factors (e.g., local weather conditions, socio-economic conditions, dietary habits, personal hygiene, and susceptibility to different pathogen strains). Climate variations are considered to be one of the key environmental factors affecting the incubation and survival of Shigella. We found that a 1°C increase in average temperature was associated with a 10.6% increase in bacillary dysentery incidence. This result was consistent with the results of previous studies. Zhang et.al. found that in Jinan City in northern China, bacillary dysentery incidence increases by 12% with a 1°C increase in maximum or minimum temperature . Checkley et al. found that diarrhea incidence increased by 8% per 1°C increase in mean ambient temperature . Bacteria replicate faster in a higher temperature environment, and food is prone to deteriorate in these environments.
We found that precipitation is another climate factor that affects the transmission of bacillary dysentery. A 1 mm increase in precipitation was associated with a 0.5% increase in bacillary dysentery incidence. Precipitation may exacerbate the transmission of enterovirus-related diseases by affecting replacement of pathogens in contaminated drinking water. However, the results of some studies have drawn opposite conclusions regarding the effect of precipitation on bacillary dysentery. Huang et al. found that there was a positive association between precipitation and the spread of bacillary dysentery . Li et al. found that there was a negative relationship .
Socio-economic conditions are another group of factors that affect the transmission of the disease. In this study, GDP per capita was selected as a proxy variable for hygiene behavior or public health condition of a region. Population density was selected as a proxy variable for frequency of contact between people, which will accelerate the transmission of viruses. The result indicated that GDP per capita has a negative association with the disease. The disease incidence decreased by 20% with a GDP per capita increase of 1 million yuan. Population density was positively associated with disease incidence; the incidence decreased by 240% with a population density increase of 1000 persons per square kilometers. This result suggests that improvement in living conditions would reduce disease transmission. Preventative strategies should be concentrated in areas with high population densities.
The results of the SaTScan indicated that the most likely spatial clusters were mainly located in the urban regions of Beijing (RR = 2.18, p-value = 10-17). The population density was very high in this area. The result was consistent with the finding of the factor analysis using SLM, in which the population density was positively associated with bacillary dysentery. The outbreak detection analysis revealed that the main outbreaks located in suburban Beijing and Tianjin and some areas of Tangshan during April and May 2011 coincided with the two important Chinese festivals and holidays (i.e., the Spring Festival and the May Day holiday). Migrant workers mainly reside in the suburban areas, where the socio-economic conditions are relatively poor compared with conditions in the urban areas. Thus, when combined with the peak tourist season, conditions are created that are favorable for a disease outbreak. The effectiveness of public health department interventions and prevention strategies would benefit from clearly defining risk areas and space-time locations for outbreaks.
There were also some limitations of this study. There have been improvements in the reporting of notifiable infectious diseases by Chinese medical facilities, but under-reporting may have occurred during the disease surveillance process . For example, although the notifiable infectious disease surveillance system included the mobile population, some individuals (e.g., tourists who visited for only a short period of time) may be hospitalized after they return home. The proportion of the mobile population in the total reported bacillary dysentery cases could not be assessed using the dataset. Underreporting would weaken the associations between risk factors and bacillary dysentery incidence. Therefore, it is possible that the results of this study represent an underestimate of the true values. In future studies, the under-reporting rate should be estimated and included in the study. A second study limitation was that bacillary dysentery may be significantly affected by micro-environments. A county-level spatial scale was used in this study because no data were available for smaller areas (e.g., the village or town level). The spatial scale used may have obscured some factors via the ecological fallacy effect .
In summary, bacillary dysentery was widespread throughout the Beijing–Tianjin–Tangshan region during 2011, and represents a serious threat to human health in this region. Effective public health measures can be implemented if they are based on a deep understanding of the epidemic characteristics, spatial-temporal clusters, and factors that affect bacillary dysentery incidence. We found that meteorological and socio-economic factors have affected the transmission of bacillary dysentery. The success of bacillary dysentery prevention and control department strategies would benefit from giving more consideration to climate variations and local socio-economic conditions.
This study was supported by the following grants: 2014 M550817; MOST (2012CB955503; 2009ZX10004-201; 201202006); NSFC (41023010), the China Postdoctoral Science Foundation (2014 M550817) and NSFC (41101431). The funders had no role in the study design, data collection and analysis, the decision to publish, or preparation of the manuscript.
- von Seidlein L, Kim DR, Ali M, Lee H, Wang X, Thiem VD, Canh DG, Chaicumpa W, Agtini MD, Hossain A, Bhutta ZA, Mason C, Sethabutr O, Talukder K, Nair GB, Deen JL, Kotloff K, Clemens J: A multicentre study of Shigella diarrhoea in six Asian countries: Disease burden, clinical manifestations, and microbiology. Plos Med. 2006, 3 (9): 1556-1569.View ArticleGoogle Scholar
- Niyogi SK: Shigellosis. J Microbiol. 2005, 43 (2): 133-143.PubMedGoogle Scholar
- Clemens J, Kotloff K, Kay BA, Organization WH: Generic Protocol to Estimate the Burden of Shigella Diarrhoea and Dysenteric Mortality. 1999, Geneva: Department of Vaccines and Other Biologicals, World Health OrganizationGoogle Scholar
- Wang XY, Tao FB, Xiao DL, Lee H, Deen J, Gong J, Zhao YL, Zhou WZ, Li ZM, Bing SH, Song Y, Ma JM, Wang ZJ, Su PY, Chang N, Xu JH, Ouyang PY, von Seidlein L, Xu ZY, Clemens JD: Trend and disease burden of bacillary dysentery in China (1991–2000). B World Health Organ. 2006, 84 (7): 561-568. 10.2471/BLT.05.023853.View ArticleGoogle Scholar
- Sharma A, Singh SK, Kori L: Molecular epidemiological characteristics of shigella spp. Isolated from river Narmada during 2005–2006. J Environ Health. 2009, 71 (6): 61-66.PubMedGoogle Scholar
- Sui JL, Zhang J, Sun JL: Surveillance of bacillary dysentery in China, 2009. Dis Surveill. 2010, 25: 947-950.Google Scholar
- Li ZJ, Wang LG, Sun WG, Hou XX, Yang HY, Sun LN, Xu S, Sun QZ, Zhang JS, Song HB, Lin HL: Identifying high-risk areas of bacillary dysentery and associated meteorological factors in Wuhan, China. Sci Rep-Uk. 2013, 3: 1-6.Google Scholar
- Curriero FC, Patz JA, Rose JB, Lele S: The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948–1994. Am J Public Health. 2001, 91 (8): 1194-1199. 10.2105/AJPH.91.8.1194.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Bi P, Hiller JE: Weather and the transmission of bacillary dysentery in Jinan, northern China: A time-series analysis. Public Health Rep. 2008, 123 (1): 61-66.PubMedPubMed CentralGoogle Scholar
- Zhang Y, Bi P, Hiller JE, Sun Y, Ryan P: Climate variations and bacillary dysentery in northern and southern cities of China. J Infection. 2007, 55 (2): 194-200. 10.1016/j.jinf.2006.12.002.View ArticleGoogle Scholar
- Kulldorff M: A spatial scan statistic. Commun Stat Theory. 1997, 26 (6): 1481-1496. 10.1080/03610929708831995.View ArticleGoogle Scholar
- Kulldorff M: Spatial scan statistics: models, calculations and applications. Recent Advances on Scan Statistics and Applications. 1999, Boston: BirkhäuserGoogle Scholar
- Kulldorff M, Heffernan R, Hartman J, Assuncao R, Mostashari F: A space-time permutation scan statistic for disease outbreak detection. Plos Med. 2005, 2 (3): 216-224.View ArticleGoogle Scholar
- Fischer M, Getis A: Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. 2010, Berlin: Springer VerlagView ArticleGoogle Scholar
- Haining RP: Spatial Data Analysis: Theory and Practice. 2003, Cambridge: Cambridge University PressView ArticleGoogle Scholar
- Moran PA: Notes on continuous stochastic phenomena. Biometrika. 1950, 37 (1/2): 17-23. 10.2307/2332142.View ArticlePubMedGoogle Scholar
- Wang JF, Christakos G, Hu MG: Modeling spatial means of surfaces with stratified nonhomogeneity. Ieee T Geosci Remote. 2009, 47 (12): 4167-4174.View ArticleGoogle Scholar
- Millo G, Piras G: Splm: spatial panel data models in R. J Stat Softw. 2012, 47 (1): 1-38.View ArticleGoogle Scholar
- Checkley W, Epstein LD, Gilman RH, Figueroa D, Cama RI, Patz JA, Black RE: Effects of El Nino and ambient temperature on hospital admissions for diarrhoeal diseases in Peruvian children. Lancet. 2000, 355 (9202): 442-450. 10.1016/S0140-6736(00)82010-3.View ArticlePubMedGoogle Scholar
- Huang DS, Guan P, Guo JQ, Wang P, Zhou BS: Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis. BMC Infect Dis. 2008, 8: 1-6. 10.1186/1471-2334-8-1.View ArticleGoogle Scholar
- Openshaw S: The Modifiable Areal Unit Problem. 1984, Norwich: Geo Books, CATMOG 38:Google Scholar
- The pre-publication history for this paper can be accessed here:http://0-www.biomedcentral.com.brum.beds.ac.uk/1471-2458/14/998/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.