Effect of ambient temperature on incidence of tuberculosis and effect modification by meteorological factors in Jinan, China during 2012-2015

Objective For assessing the nonlinearity and delayed effect of temperature on incidence of Tuberculosis (TB) and effect modification by meteorology factors, daily data on meteorological factors, air pollutants and incidence were obtained in Jinan, China, from 2012 to 2015. Methods A distributed lag non-linear model (DLNM) combined with quasi-Poisson regression model was employed to assess the nonlinearity and the delayed effect of associations. We further built a series of weather-stratified models categorizing the meteorology factors into two levels to assess the effect modification of the ambient temperature effect. Results The correlation between tuberculosis (TB) cases and daily average temperature (T mean ) was nonlinear with a delayed effect. At the current day (lag 0), the increase of T mean decreased the risk of TB incidence; over lag 0-70 days, the decrease of low T mean and the increase of the high T mean both indicated the increased risk of TB. The cold temperature showed an immediate effect at the current day, with a harvesting effect in the following days. There was no significant harvesting effect in hot effect. Meanwhile, the effect of hot temperature on TB appeared with an about two-week lag and was lower than cold effect. The effect modifications by relative humidity, wind speed and sunshine duration were observed. Conclusion Results indicate that there was a nonlinear correlation with a harvesting effect between temperature and TB in Jinan, and both cold effect and heat effect exist the delayed effect.


Introduction
In the past decades, the prospect of continued global warming, climate change, serious pollution and extreme weather events has concentrated attention on the harmful impacts of environment on public health. Many studies [1][2][3] have reported an increased mortality caused by high or low temperature. However, previous studies mainly focused on the relationships between meteorological factors and chronic diseases, such as respiratory diseases (RD) [4], cardiovascular diseases (CVD) and myocardial infarction [5]. With growing concerns about climate change, an increasing number of studies also began to focus on associations of weather variability with the fluctuations of infectious diseases and suggested that weather factors play an important role in infection incidences [6], such as hand-foot-mouth disease [7], Zika virus infection [8] and diarrhea [9].
Worldwide, Tuberculosis (TB) is one of the top 10 causes of death and the leading cause from a single infectious agent (above HIV/AIDS). Millions of people continue to fall sick with TB each year. Two thirds of cases were in eight countries with the highest rates in India (27%), China (9%) and Indonesia (8%). China is not only the second largest country with the highest number of cases, but also one of the three countries with the largest numbers of multidrug-resistant and rifampicin-resistance (MDR/RR-TB) (13%).
[10] TB remains an ongoing intractable health challenge in China.
TB spread pattern is influenced by geographic and social factors, which indicated it is necessary to assess the impacts of temperature on TB in various regions. Seasonal fluctuations in TB notifications have been reported from a number of researches [11][12][13], these studies also suggest delayed effects of environmental factors. Further, it has been 4 shown that the risk of TB has a correlation with climate and extreme heat or cold temperatures. [14,15] Due to the diversity of temperature ranges and fluctuations, climate types, and economic environments in different regions, the relationships between temperature and TB in different regions should be studied and will provide important evidence also for other countries.
Moreover, a lot of studies in different regions put forward that other environmental factors can also exert an effect on TB incidence. For example, the areas with extra dry climate are high-risk regions of TB [14]; the decrease of SD lead to an increased risk of TB [16]; the TB incidence are positively associated with the WS [17,18]. The meteorological and environmental factors are some of the central variables affecting the airborne transmission of pathogens. [19,20] Yet there have been only limited studies on effect modification by other meteorological factors on temperature effect on TB.
In addition to meteorological factors, air pollution has also been linked to TB risk. The effects of carbon monoxide (CO) and particulate matter less than 2.5 µm in aerodynamic diameter (PM 2.5 ) on incidence of TB were significant. [21][22][23] In South Korea, the exposure to high concentrations of suspended particles increased at 1.27 times the incidence of TB [24]. However, many studies [4,14] of ambient temperature and health outcome did not account for air pollutants, and in the previous review [25], it was not clear from the few studies conducted whether air pollutants acted as confounders, effect modifiers, or both.
It is critical to control the effect of pollutants in models with ambient temperature, since they may often exert the influence on a daily basis [26]. On the other hand, Jinan is a typical heavily polluted area, and the relationship between cases of TB and pollutants have been determined in Jinan. Thus, the actual association between ambient temperature and incidence can be observed, only after controlling pollutants in the models.
Here, this study aimed to assess the effect of ambient temperature as well as delayed effect on TB based on the infectious disease surveillance data in Jinan by using distributed lag non-linear model (DLNM) controlling the effects of the pollutants affecting the infection of TB. Although almost all studies that examine the effects of air pollution and mortality have controlled for meteorological factors, control for air pollution in studies assessing the effects of temperature has been rare. Simultaneously, based on the relationship between T m ean and incidence of TB, this study also investigated if the other meteorological factors modify the temperature-TB incidence relationship. It will help plan effective intervention strategies for the prevention and control of TB in similar populations and help public health professionals to make response.

Study area and population
Jinan is located in the Mideast of China (36°01′N to 37°32′N, 116°11′E to 117°44′E). It towards the south is Tai Mountain, whilst the north is bordered by the Yellow River.
Belonged to the warm temperate continental climate, four seasons. It is dry and rainless in spring, hot and rainy in summer, cool and sunny in the fall, freezing and dry in winter.
As the capital city of Shandong province, Jinan is the center of politics, economies and communication in the province. The total area is 7,998 km 2 , and the total population Daily mean hourly air pollutants data (inhalable PM 2.5 and CO) were obtained from the Jinan Environmental Monitoring Center. The data was obtained from 14 fixed monitoring stations, spanning the entire region, including 11 sites located in urban areas and 3 sites located in suburban county areas. Daily average values of air pollution were used in this study and calculated the average from above 14 fixed monitoring stations.

Statistical analysis
Firstly, a descriptive analysis was performed to describe the distribution of TB cases, meteorological factors and pollutants during the study period. The minimum, maximum, quartiles, mean and standard deviation were calculated. The associations of TB cases with meteorological factors and air pollutants were assessed by Pearson's correlation test.
Factors related to the incidence of TB were included in the model.
Secondly, the effect of T m ean on TB cases was estimated utilizing a distributed lag nonlinear model (DLNM) with quasi-Poisson regression. DLNM, a flexible modelling framework, describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. [31]. As potential confounders, longterm trend/seasonality (by using day of study), day of the week (DOW), public holiday (HOD), RH, WS, SD, CO and PM 2.5 were considered. Their effects were removed on by using smooth functions to calculate net effect of T m ean on incidence of TB. [32] The degrees of freedom (df) of splines in different functions were automatically selected by Generalized Approximate Cross-Validation (GACV). The Pearson's correlation test and collinearity diagnosis were used to analyze the correlation and collinearity between the various factors. Generally, when |r s | ˃0.8, it is considered that there is a strong correlation between factors; when the variance inflation factor (VIF) ≥ 10, it is considered that there may be a serious collinearity between factors [33,34].
Instead of using a linear term, a cross matrix for the daily temperature was established to represent the non-linear and delayed effect. We selected a natural cubic spline basis to model the non-linear effects using three df in the temperature space, and the polynomials with four degrees to examine the delayed effect. Model selection for lag structure was carried out by minimizing the GACV criteria; these lag structure (3, 4, 5, 6, 7, 8, 9, [14,35] and GACV values, we set a maximum lag structure as 70 days (10 weeks) as it also is longer than the incubation period (4-8weeks). We used a temperature of 15.0 °C (which was mean value of the T m ean in Jinan, 2012 to 2015) as the reference value to calculate the relative risks (RRs), and used the minimum, the 5th and 25th of percentiles temperature as the cold temperature effect and the maximum, the 95th and 75th of percentiles temperature as the hot temperature effect.
Thirdly, a weather-stratified DLNM was developed to quantify the effect modification by other meteorological factors (RH, WS and SD). We used this model to estimate the temperature effect for two meteorological factors strata: <50th percentile and > 50th percentile. Meanwhile, in the research process, it was found that the effects of temperature ranging from the 25th to 75th quantile were basically not significant. We selected a double threshold function as basis to model the cold and hot temperature effects with the 25th and 75th quartiles as the cut-off points. We used the interval as a reference to estimate the cold temperature effect (the 5th percentile) and hot temperature effect (the 95th percentiles) by the RR with 95% CI. Further, we also insert an interaction function of T m ean with RH, WS or SD to identify whether the exists of effect modifications are due to chance. [36]

Sensitivity analyses
To check the robustness and validity of the main findings of this study, sensitivity analyses were performed by adjusting df of temperature (df = 2,4,5), fitting the models to TB cases at lag 0 and lag 0-70 days. Further, we also conducted sensitivity analyses by adjusting one environmental factor at a time or excluding all air pollution factors (PM 2.5 , CO) or all meteorological factors (RH, WS, SD) from the model. In this study, the relative risk (RR) and 95% confidence interval (95% CI) were used as the evaluation indexes of the effect. The analysis was performed by Stata software (version 15.0) and packages (splines, DLNM, mgcv) of R statistical software (version 3.5.2).
Statistical significance was set at P < 0.05.

2.7
The relationships between T m e a n and number of TB cases Figure 2 illustrates the three-dimensional graph of a nonlinear relationship between T m ean and TB cases, with reference at 15.0℃. An immediate effect of the minimum temperature was observed on the current day (lag 0), with lower risk in the following days. Figure 3 presents the lag-response relationships between different T m ean levels (minimum, the 5th, 25th, 75th and 95th percentiles and maximum temperature) and incidence. The effect of the minimum temperature (-9.4 ℃) led to the risk in TB incidence at lag 0 and the second High RH increased the risk of TB in hot temperature situation after lag 21 and in cold temperature situation after lag 70, respectively; meanwhile, low RH decreased the risk of TB in cold temperature situation after lag 21 and in hot temperature situation after lag 70.
In addition, low WS increased the risk for different temperature at different lag period; and high WS decreased the risk in hot temperature at lag 49. Furthermore, low SD increased the risk in cold temperature situation at lag 21. By verifying the interaction terms, the interactions of RH (P = 0.001), WS (P = 0.004) and SD (P = 0.02) with T m ean were significant, respectively.

Sensitivity analyses
12 Table.S2 contains details of the results from the sensitivity analyses. When we changed df (2, 4, 5 df) for the temperature space in the DLNM, the estimated changes were slightly smaller. Adjusting for meteorological factors slightly changed the overall cumulative RRs, whereas adjusting for air pollution gave slightly larger in cold effect but still did not change substantially. Our sensitivity analyses suggested that the results were not dependent on modeling assumptions.

Discussion
We examined the effects of ambient temperature on TB cases in Jinan, one of the so-called four "ovens" with serious air pollution in mid-eastern China, during the period of 2012 to 2015. Study findings indicated that the temperature-incidence relationship was non-linear, with showing an S-shape at the current day and a U curve over lag 0-70 days. Further, the minimum T m ean effect appeared immediately with a following harvest effect, and the second onset peak appeared after lag 8-9 weeks, whereas the maximum T m ean effect became predominant with about two weeks' lag. Meanwhile, the T m ean effect on incidence of TB modified by different levels of RH, WS and SD, and varied across different lag period.
Our results of a negative and non-linear relationship between ambient temperature and notified cases of TB infection are consistent with research carried out in other countries with different weather conditions [14,17,35]. We also found that the risk of TB incidence was greatly affected by extreme temperature on the current day. On the other hand, the overall cumulative effect showed trends for increased risks for decline of cold temperature and increase of hot temperature.
Many investigators [14,17,37] have reported the delayed effect in the relationship between TB and cold or hot effect. Our study also confirmed that the delayed effect 13 existed. For the effects of the minimum and the 5th percentile of T m ean , the immediate effects appeared at that day, and the second onset peak appeared at lag 8-9 weeks. For the effects of the 75th, 95th percentiles and maximum of T m ean , the onset peak appeared after lag about two weeks. The peak of cold effect appeared earlier than that of hot effect, which is comparable to the results of a study [14] conducted in Japan. In contrast to this study's findings, however, the results in the Japan study showed that high temperature effects were generally constant at lag periods of up to 12 weeks, whereas the effects in low temperature ranges were persistent over shorter lag periods and diminished over time. This may be due to the warm climate in Japan, so the 5th percentile temperature (5.4 °C) in the Japan study was only equivalent to the 25th percentile in our study.
Yuanyuan Xiao [35] found that average temperature was inversely associated with TB incidence at a lag period of 2 months. Similarly, we found that T m ean under 15 °C was also negatively associated with TB at lag 63, but hot effect was not significant.
The difference in lag effects as our results of DLNMs suggested would be also related to some characteristics, and some researchers have provided this context for interpreting our results. Fares [38] manifested that lower temperature during winter may induce the susceptibility to respiratory epithelium infection. The fluctuation in weather temperature during winter may also act on the respiratory epithelium by slowing mucociliary clearance and inhibiting phagocytosis, causing pathophysiological responses, which then lead to increase the susceptibility to infection. [39] In addition, in winter in Jinan, the citywide coal-burning heating exacerbates smog, which would increase the number of carriers that can spread pathogens [20] and increase the risk of RD; Liu [23] provided the evidence that heavy pollution are positively correlated with TB incidence. Furthermore, Naranbat [13] hypothesized that temperature may change the time people spend at home or 14 outside. China is a populous country, people gather, and close door and windows during the cold winter, and the crowded indoor environment is also a risk factor for infectious diseases. As the temperature gradually increases with an agreeable weather, citizens were more willing to play outside and open the window for ventilation. Meanwhile, the heat of the summer might trigger a thermal reaction, but it also comes with a reluctance to congregate for residents, preferring to stay indoors with air conditioning. Which may not induce the high risk of transmission of tuberculosis as cold temperature do.
We found some evidence of harvesting effect in our study; there was an incidence deficit for the minimum and the 5th percentile T m ean at the lag about 3 weeks. We speculate that the harvesting effect would support the mechanism of temperature influencing the incidence risk. In particular, it may be that presents in extreme cold temperature only hasten the TB incidences of individuals in a small, frail, infected subset of the population who will attack even in the absence of extreme cold effect. A possible reason is extreme cold air attacks the body's respiratory and immune systems, speeding up the onset of TB to infected people. In contrast, hot temperature might mainly disturb the body's cardiovascular system, and has little direct effect on the respiratory system. [40] However, meteorological factors may play an important role. Our findings showed that low RH decreased the risk of TB for temperature which was different from Yingjie Zhang's research [41]. In cold temperature situation, the increased RH may create a suitable environment for the growth and reproduction of tuberculosis. Our study also suggested that low WS could increase the effect of low T m ean on TB at the current day and at lag 70.
The higher WS could accelerate ventilation, dilute the concentration of bacteria and help reduce the risk of becoming infected. Although another study [18] indicated that areas with stronger wind speeds tend to have a higher infection risk, our study findings were supported by the findings of Kai Cao [42]. As has been found in a few other studies [17,42,43], the low SD would raise the risk of TB. Our findings showed that the low level of SD positively modified on cold temperature effect. We speculate that this result would be also related to some view point indicated by these studies [44,45] on TB that low serum vitamin D levels were associated with higher risk of active tuberculosis. The low SD would affect the absorption of vitamin D for public. However, there was still a lack of validation of biological mechanisms of vitamin D on TB, which should be a further direction.
A study limitation is the use of data on temperature and air pollution from fixed monitoring sites rather than measuring individual exposure, which would bring about measurement errors because individual exposure temperature may be not entirely identify with outdoor average temperature. Secondly, cold effect and hot effect was calculated by comparing the 5th to the 25th percentile and the 95th to the 75th percentile temperatures. This accounted for the effect of cold and hot temperature to some extent.
But the reason for this way is that the study population is not sensitive to T m ean ranging from the 25th to the 75th percentile, there may be inappropriateness when extrapolating calculating method to an unequal population or other diseases. Because the complexity of other factors and the difference of population adaptation. In addition, we only used data from Jinan to examine the effects of temperature on incidence of TB so the findings may not be generalizable to other areas.
In conclusion, tuberculosis incidence in Jinan was found to be nonlinear and negative    The effects of daily average temperature (℃) on incidence of tuberculosis along days of lag, adjusting for PM2.5, CO, relative humidity, wind speed, sunshine duration, public holiday, day of the week, and time trend. The continuous curves are relative risks of incidence comparing the minimum, 5th, 25th, 75th, 95th and maximum percentile of temperatures with 95% CI (shaded area) 29 Figure 3 The effects of daily average temperature (℃) on incidence of tuberculosis along days of lag, adjusting for PM2.5, CO, relative humidity, wind speed, sunshine duration, public holiday, day of the week, and time trend. The continuous curves are relative risks of incidence comparing the minimum, 5th, 25th, 75th, 95th and maximum percentile of temperatures with 95% CI (shaded area)  Appendix Tables 1 -2.docx   Appendix Tables 1 -2.docx