Study on association factors of intestinal infectious diseases based-Bayesian spatio-temporal model

Intestinal infectious diseases (IIDs) are common in China. According to the Chinese Center for Disease Control and Prevention, the incidence of intestinal infectious diseases in China was around 100 per 100,000 population in 2006 and around 240 per 100,000 population in 2017 [1], causing a considerable disease burden.

During our study period, the change of occurrences in IIDs accompanied by the promotion of harmless sanitary, the upsurge of economics, and the change of climate under the background of global warming. Domestically, there are distinct regional differences in climate, environmental conditions and urbanization and variation in infectious diseases is to be expected across the country. Previous studies have assessed the connection between these factors and the morbidity of IIDs. For example, Chen et al. found that sanitary toilet use had an impact on IIDs in Jiangsu Province in China [2]. Li et al. identified the association between meteorological factors and bacillary dysentery in Beijing [3]. However, those studies were limited to a single disease and local areas and lacked geographical integrity and disease comprehensiveness. Thus, we intended to conduct systematic and national research to find out how these factors affected the occurrence of IIDs.

Addressing this issue requires a comprehensive approach that takes into account regional differences and factors that contribute to the occurrence of IIDs. To achieve our goals, we applied the Bayesian hierarchical spatio-temporal model. Our datasets contained various scales of spatial and temporal variability and involved many observation locations, which could limit the effectiveness of traditional general regression models [4]. Therefore, the model with space, time, and space-time interaction structure was needed. The Bayesian hierarchical spatio-temporal models not only fulfilled the requirements but provided simple strategies for incorporating complicated hierarchy within the Markov chain Monte Carlo framework. Several studies have applied this model to disease surveillance datasets. For instance, Cao et al. used the spatio-temporal model to analyze the influential factor for tuberculosis in mainland China [5]. Liu et al. applied Bayesian spatio-temporal analysis to study the association of air pollutants with tuberculosis in Hubei Province in China [6].

In this study, we aim to describe the spatio-temporal trends of IIDs in the Chinese mainland and investigate the association between sanitary, socioeconomic, and meteorological factors with IIDs. Thus, to better understand the pattern of IIDs and guide the strategic direction of government prevention.

To our knowledge, this study is the first to systematically show the influence of socioeconomic and meteorological factors on nine IIDs, across the vast region in mainland China. We showed the spatial and temporal distribution of IIDs incidence and found associations between factors and IIDs.

The descriptive results suggest that, during the study period, the occurrence of most IIDs has dramatically reduced, with uneven reductions in different diseases, while we observed increases in hepatitis E, HFMD, and other infectious intestinal diseases. That was consistent with a previous descriptive study on IIDs in China which found that class B IIDs were prevented with high efficiency while class C IIDs experienced a higher incidence rate and gradually increasing trend [13]. In developed countries, HAV infection incidence tends to decrease while HEV infections are increasingly observed, consistent with our findings as well.

Notably, throughout our study period, HFMD has been classified as a Class C infectious disease in China since 2008, mandating its reporting within 24 hours [14]. Consequently, the improvement in the surveillance system has resulted in an increase in the reported cases of HFMD. Additionally, the notifiable disease surveillance system network has undergone expansion, with gradual enhancements in diagnostic capacity, criteria, surveillance data reporting methods, and overall sensitivity [15]. As a result, there has been an overall increase in the reporting of other diseases, such as OIIDs. Therefore, it is crucial to consider these factors carefully when discussing and interpreting the findings of our study.

The findings suggested that ARHST had an inconsistent effect on IIDs. An increase in ARHST led to a decline in the occurrence of typhoid and paratyphoid, but an increase in morbidity of bacillary dysentery, and other intestinal infections. In addition, we found no significant effect of ARHST on the occurrence of cholera, amoebic dysentery, hepatitis A, hepatitis E, and HFMD. Empirical results of previous studies showed inconsistent effects as well. For example, in Jiangsu Province in China, a study found that the cumulative number of households using sanitary toilets was negatively associated with the aggregate annual incidence of class A and B IIDs but ineffectively with class C IIDs [2]. A study in Fiji found that sanitary conditions served as protective factors in the occurrence of typhoid fever [16]; Zuin found that low hygienic conditions are associated with high HAV and HEV seroprevalence [17]; besides, Knee et al. found their latrine intervention did not affect the bacterial intestinal infection in Mozambique [18]. Thus, findings from our study and others indicated that sanitary interventions might not be able to curb the spread of all kind of IIDs. However, a study found that latrine interventions have rarely been shown to prevent diarrhea, according to their historical review and meta-analysis. Their explanation for this conclusion was that environmental intervention and implementation contexts were too complex to average and required a better understanding of their effects [19]. This viewpoint might help us to explain our result. Due to the limitation of our ecological study, we averaged the effect of ARHST based on the unit of the province, which could mask the crucial environmental differences.

Urbanization led to an increase in the occurrence of hepatitis E and showed no significant effect on the occurrence of the rest IIDs in our findings. However, empirical results in previous studies tended to regard urbanization as a protective factor. For example, Masoud et al. found the protective effects of urbanization on typhoid fever in Iran [11], and Ceran et al. found urbanization associated with the reduction of hepatitis A seropositivity in Turkey [20]. It might be the lack of other related confounders like male sex and underlying conditions [21] that caused this inconsistency. Another explanation was that urbanization could be related to the increase in consumption of pork or other meat products [22], as zoonotic transmissions of hepatitis E were increasingly observed [23]. Nevertheless, few studies conformed with our results. A study in Australia also found no statistically significant association between urbanization and the prevalence of infectious gastroenteritis illness [24]. Jiang et al. studied the urbanization effect on population health in China and found that when GDP reached a certain threshold, it would reduce the health-prompting effect of urbanization in inland and northern provinces. [25] Therefore, our result on urbanization could be the consequence of this reduction effect by GDP. For GDP, we observed a negative association between GDP and morbidity of paratyphoid fever, bacillary dysentery, and hepatitis A, and no significant association between GDP and morbidity of the rest IIDs. Some previous studies assessed the protective effect of GDP on IIDs. Zuin found that socioeconomic level is negatively associated with HAV seroprevalence [17]. Masoumi et al. identified socioeconomic status as the key determinant in the downfall of typhoid cases [26]. However, a study in Guangzhou province in China found no significant effect of GDP on typhoid [27], in accord with our result of typhoid. The further interpretation was that socioeconomic status does not affect the health of all residents equally, especially when our study units were as big as a province. Nevertheless, we did confirm the protective effect of GDP on some IIDs, pointing forward to future intervention.

In the study, a significant effect of population density on the occurrence of all IIDs was not found. However, it is worth noting that previous studies have reported inconsistent results in this regard. For instance, Du found a positive association between higher population density and increased incidence of HFMD in Guangzhou province [28]. Similarly, Xu identified population density as an important determinant of bacillary dysentery in the Beijing-Tianjin-Hebei region [29]. One plausible explanation for the disparity in findings could be the scale at which the studies were conducted. The study areas in both Du and Xu's research were based on smaller spatial units within the province, allowing for a more detailed analysis of population density effects. In contrast, our study employed larger provincial units, potentially averaging out the effects of population density, which may vary significantly within each province, particularly between urban and rural areas.

The results exhibited a significant effect of meteorological factors on IIDs as well. According to previous studies, there are various postulations on the way meteorological factors affect the occurrence of IIDs that is via microorganism activity and human behavior. Based on these assumptions, the reason why meteorological variables had different effects on IIDs was that the sensitivity to changes in heat, moisture, nutrients, and related conditions was different in pathogens [30].

In findings, a temperature rise elevated the incidence of typhoid, paratyphoid, and HFMD, consistent with previous studies that identified that warmer climate conditions would lead to the increase of HFMD [31], diarrhea [32] and typhoid [33] and paratyphoid fever [33]. Warmer conditions might increase bacterial pathogen loads in animal reservoirs and prolong transmission seasons [34]. Higher temperature also leads to an increase in food spoilage and change in the behavior of hosts, like altering consumption practices and gathering in air-conditioned public places [35].

Precipitation was considered a risk factor in cholera and typhoid but a protective factor in bacillary dysentery in our results. In previous studies, high precipitation acted as a risk factor for IIDs [36], as it was conducive to contaminating water, fruits, and vegetables with feces. These empirical results were consistent with our findings on cholera and typhoid but contradictory to bacillary dysentery. The probable interpretation was the “dilution effect” brought by Levy et al. that when rainfall lasts long, it might dilute microbial concentrations [37].

We observed a negative association between humidity and cholera, typhoid, hepatitis A, and other intestinal infectious diseases and a positive association between humidity and amoebic dysentery, discrepant with previous studies in Yunnan and Kolkata. Kim et al. found that enteric viruses and HAV became inactive in higher relative humidity levels, which might help explain our findings [38].

We observed a negative association between wind speed and cholera, paratyphoid, bacillary dysentery, amoebic dysentery, hepatitis A, and other intestinal infectious diseases. Few studies found a significant association between wind speed and IIDs, except for a study in Beijing that found a similar negative association between wind speed and bacillary dysentery [3]. Due to the lack of related studies, we could not interpret the detailed mechanism of how wind speed affects the transmission between pathogens and hosts. However, we noticed a positive association between wind speed and HFMD. Though, unlike other IIDs, the result conformed with a study in Hongkong [39]. The probable reason might be the different transmission of HFMD and the rest IIDs. Since the droplet transmission of HFMD, Ma et al. reckoned that wind speed might favor the spread of disease through respiratory droplets.

There are some limitations to consider in this study. First, our data is in the annual and provincial units where the averaged data could not accurately reflect the causal association between determinants and diseases. Second, our research is based on provincial units, which are administrative divisions. However, due to the vast geographic expanse of the Chinese mainland, it encompasses several climatic zones. We propose the possibility of conducting research based on these climatic zones or subdividing our model into multiple climatic zones. This approach would enable us to perform a more detailed analysis of meteorological variables and examine their relationships within different climatic zones. Moreover, the associations between our factors and IIDs were complex and involved various confounders. The variables included in this study were limited, for example, the absence of food consumption data. Thus, we hope to obtain more related variables in our future study. In general, due to the limitation of ecological study, our results are not reliable enough to build solid causal inferences. Therefore, future studies should base on the individual level and consider more rigorous research.

In conclusion, this study systematically and quantitatively studied the effect of socioeconomic and meteorological factors on IIDs, which provided causal clues for future studies and guided government planning.