Tuberculosis (TB) is an infectious disease caused by the bacillus Mycobacterium tuberculosis, which is still a leading cause of death in low-income and middle-income countries, and remains a major public health burden in many developing countries [1]. China (0.9 million-1.2 million) alone accounted for an estimated 12% of all incident TB cases worldwide in 2010 [2]. Linyi City is one of the high-incidence areas in China, and there is an estimated 6,000 reported cases every year from 2005–2010. Thus, preventive strategies are urgently needed in the development of TB.

In recent years, geographical information systems (GIS) and spatial analysis were frequently used to describe the pattern of TB. In India, purely spatial and retrospective space-time analysis were used to find significant hotspots of TB in three areas of the Almora district [3]. In Japan, space-time scan statistics identified TB clusters in Fukuoka [4]. In Madagascar, spatial clustering of TB was associated with socio-economic and patient care factors in Antananarivo City [5]. With respect to spatiotemporal clustering, three high incidence space-time clusters were identified in Portugal between 2000 and 2004 [6]. In South African, GIS and spatial analysis were used to study TB transmission patterns in a high-incidence area [7]. In an Urban West African, spatial scan statistic was used to assess purely spatial and space-time clusters of TB in Greater Banjul [8]. Another study in Brazil investigated spatial patterns of the incidence of TB and its relationship with socio-economic status [9]. In Beijing, GIS and spatial analysis were used to determine the role of migration in the transmission of TB [10]. To our best knowledge, there were few studies to explore the spatial epidemiology of TB in Linyi City, China. A better understanding of the spatial epidemiology of TB may help health departments to provide guidance for formulating regional prevention and control strategies [9, 10].

The spatial analyses, such as spatial smoothing, spatial autocorrelation and cluster analysis are commonly used to characterize spatial epidemiology of diseases. Spatial smoothing was used to reduce random variation associated with small populations [9, 11–14]. Spatial autocorrelation analysis was performed to detect significantly difference from a random spatial distribution [10, 15–17]. Spatial cluster analysis was conducted to identify whether cases of disease were geographically clustered [4, 8, 14, 18–25].

Therefore, we conducted GIS-based spatial analyses involving spatial smoothing, spatial autocorrelation analysis and space-time scan statistic to characterize geographic distribution pattern of TB in Linyi City of China during 2005–2010.

In our study, exploratory spatial data analysis and spatial cluster analysis of TB were conducted at town level in Linyi City, China. We mapped TB from different aspects such as crude incidence, excess risk, and spatial empirical Bayes smoothed incidence, investigated the spatial pattern and highlighted geographic areas with significant high incidence of TB in Linyi. The study showed that the spatial distribution of TB in Linyi City was nonrandom and clustered with the significant Moran’s I for each year. Local G _i ^* detected five significant spatial clusters for high incidence of TB when only space distribution was considered. However, one most likely cluster and nine secondary clusters for high incidence of TB were identified when both space and time were considered in the space-time analysis. When compared the clusters of the Local G _i ^* with those of the space-time scan statistic, both methods detected similar and significant high-risk clustering. Consistent results using these two methods, in addition to 6-years of TB case data, and a rate smoothing technique suggest that these results are robust.

The result of the present study provided useful information on the prevailing epidemiological situation of TB in Linyi City. The novel knowledge about the presence of clusters of TB in Linyi can help the Linyi Institute for Tuberculosis Control to intensify their remedial measures in the identified areas of high tuberculosis prevalence and chalk out future strategies for more effective TB control. Strategies may include compulsory BCG immunization of the children, educating the public about the dangers of TB as a re-emergent epidemic, and monitoring TB carefully.

While our study has demonstrated the usefulness of GIS and spatial analysis, it still has several limitations. First, our data relies on official surveillance and we cannot exclude the possibility that some towns may underreport the number of cases forvarious reasons. Cases might be missed by routine notification systems because people with TB do not seek care, seek care but remain undiagnosed, or are diagnosed by public and private providers that do not report cases to local or national authorities. Second, we analyzed a relatively short period of time (i.e. 6 years, from 2005 to 2010). Further studies are needed to evaluate the spatial and temporal changes in the pattern of TB using data from a longer study period. Third, we did not assess possible risk factors that could be associated with clustering. It is not a survey-based study but official surveillance, as socio-economic and environmental factors are not taken into account.

The present study only analyzed the statistically significant clusters of TB. Future researches are warranted to focus on the effect of various socio-economic and environmental factors on the high incidence of TB in the clustering areas. Disease prevalence is frequently associated with many aspects of socio-economic status, such as overcrowding [7], unemployment [7, 31], low educational level [32, 33], number of shebeens [7] and poor housing quality [34]. Moreover, spatial clustering of TB was also associated with the migrant population [10], patient care factors [5] and environmental factors [7]. After detecting the statistically significant clusters of TB in the region, a survey-based study is intended to identify the role of these factors in the spread of TB.

This study showed the presence of clusters of TB in Linyi City, China and demonstrated that using the existing health data, the spatial statistic and GIS may provide public health officials with necessary feedback about the prevalence of statistically significant clusters of TB in the region, and thus enable them to carry out more effective strategies to control TB. Since the efficacy of TB control measures in specific areas could be assessed by a longitudinal change in TB prevalence, the space-time scan statistic also can contribute to health program evaluation. More detailed individual level investigations are needed in the identified clusters to evaluate the most important determinants of disease distribution.