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    Maximum daily air temperature at three stations in or near the five villages in Sichuan, People’s Republic of China.

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    One week running average rainfall at three stations in or near the five villages in Sichuan, People’s Republic of China.

  • 1

    Spear RC, Seto E, Liang S, Birkner M, Hubbard A, Qiu D, Yang C, Zhong B, Xu F, Gu X, Davis G, 2004. Factors influencing the transmission of Schistosoma japonicum in the mountains of Sichuan Province of China. Am J Trop Med Hyg 70 :48–56.

    • Search Google Scholar
    • Export Citation
  • 2

    Zong B, Xiong ME, Zhao LG, Lai YH, Lai J, Chen L, Yin HZ, Sha KY, Zhang Y, Gu XG, 2001. Investigation of water contamination situation in Qionghai Lake area, Xichang, Sichuan. J Pract Parasitic Dis 9 :97–100.

    • Search Google Scholar
    • Export Citation
  • 3

    The Office of Endemic Disease Control M, 2000. Handbook of Schistosomiasis Control. Shanghai: Shanghai Science & Technology Press.

  • 4

    Laird NM, Ware JH, 1982. Random-effects models for longitudinal data. Biometrics 38 :963–974.

  • 5

    Scott JT, Diakhate M, Vereecken K, Fall A, Diop M, Ly A, De Clercq D, de Vlas SJ, Berkvens D, Kestens L, Gryseels B, 2003. Human water contacts patterns in Schistosoma mansoni epidemic foci in northern Senegal change according to age, sex and place of residence, but are not related to intensity of infection. Trop Med Int Health 8 :100–108.

    • Search Google Scholar
    • Export Citation
  • 6

    Gazzinelli A, Bethony J, Fraga LA, LoVerde PT, Correa-Oliveira R, Kloos H, 2001. Exposure to Schistosoma mansoni infection in a rural area of Brazil. I: water contact. Trop Med Int Health 6 :126–135.

    • Search Google Scholar
    • Export Citation
  • 7

    Li Y, Sleigh AC, Williams GM, Ross AG, Forsyth SJ, Tanner M, McManus DP, 2000. Measuring exposure to Schistosoma japonicum in China. III. Activity diaries, snail and human infection, transmission ecology and options for control. Acta Trop 75 :279–289.

    • Search Google Scholar
    • Export Citation
  • 8

    Ross AG, Yuesheng L, Sleigh AC, Williams GM, Hartel GF, Forsyth SJ, Yi L, McManus DP, 1998. Measuring exposure to S. japonicum in China. I. Activity diaries to assess water contact and comparison to other measures. Acta Trop 71 :213–228.

    • Search Google Scholar
    • Export Citation
  • 9

    Friedman JF, Kurtis JD, McGarvey ST, Fraga AL, Silveira A, Pizziolo V, Gazzinelli G, LoVerde P, Correa-Oliveira R, 2001. Comparison of self-reported and observed water contact in an S. mansoni endemic village in Brazil. Acta Trop 78 :251–259.

    • Search Google Scholar
    • Export Citation
  • 10

    Chandiwana SK, Woolhouse ME, 1991. Heterogeneities in water contact patterns and the epidemiology of Schistosoma haematobium. Parasitology 103 :363–370.

    • Search Google Scholar
    • Export Citation

 

 

 

 

 

SPATIAL AND TEMPORAL VARIABILITY IN SCHISTOSOME CERCARIAL DENSITY DETECTED BY MOUSE BIOASSAYS IN VILLAGE IRRIGATION DITCHES IN SICHUAN, CHINA

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  • 1 Center for Occupational and Environmental Health, School of Public Health, University of California, Berkeley, California; Sichuan Institute of Parasitic Diseases, Chengdu, People’s Republic of China

A mouse bioassay was used monthly over the infection season of 2001 to determine the temporal and spatial variability of schistosome cercarial density in irrigation ditches in five villages in southwestern Sichuan Province in the People’s Republic of China. Analysis of variance showed that approximately half of the variability was due to the village and site within the village, with little contribution from air temperature, weekly average rainfall, or the month within the infection season in which the bioassay was performed. The location-specific variability in these data suggest that epidemiologic studies will generally have low power to detect the influence of water-contact intensity on human parasite burden without taking account of variations in cercarial density at sites of water contact.

INTRODUCTION

In 2000, we carried out an extensive cross-sectional study of schistosomiasis transmission in 20 villages in southwestern Sichuan, People’s Republic of China aimed at identifying agricultural and environmental factors predictive of infection risk.1 In each of these villages between 6 and 16 sites were monitored for cercariae in surface water in July, the middle of the transmission season, using a mouse bioassay. Regression of the village disease prevalence in mice (averaging over all bioassay sites in a village) against prevalence in humans was highly significant (R2 = 0.85, P < 0.001). A similar result was obtained between village mean worms per mouse and human egg secretion per gram of feces as measured by the Kato-Katz method (R2 = 0.80, P < 0001). Thus, in this earlier work, we used the July bioassay data as an index of season-long infection risk to humans. However, while the July data gave insight into the spatial variation of the cercarial risk within and between villages, it did not, of course, measure temporal variation. Likewise, in the earlier study, we did not determine how the bioassay results might be influenced by environmental factors such as temperature and rainfall during or just prior to the exposure of the mice. Here we report results from five of these villages in 2001 to quantify the relative contributions of site, month, and weather to variations in cercarial risk as measured by the bioassay over one infection season. While our primary interest in the bioassay is as an index of infection risk in the context of disease control interventions, a side issue concerns the association of water contact and transmission intensity. In particular, we are interested in the influence of spatial and temporal variability in cercarial risk on estimates of that association.

The villages in which these experiments were conducted are near the city of Xichang in southwestern Sichuan. The climate is subtropical with an annual average temperature of 17°C and annual rainfall of approximately 1,000 mm, more than 90% of which falls between the beginning of June and the end of October. The landscape is largely irrigated agricultural land, with two growing seasons. The living and working style of people in a village are usually very similar, and the fields that they farm are usually adjacent to their housing areas. However, crops and farming practices do differ between villages, largely related to differences in natural terrain, soil conditions, and economic factors. In the lowland villages near Qionghai Lake (elevation = 1,520 meters), the predominant crops are rice and wheat, whereas in villages in the terraced foothills (elevation = 1,810 meters), tobacco, vegetables, and corn are more common. In all of these villages an important factor to sustaining the disease cycle is that fertilization practices make extensive use of human and animal manure that is moved from residential pit latrines to field storage pits without treatment and with minimal holding times.

MATERIALS AND METHODS

The five villages that were selected for these bioassay studies cover the range of topographic, agricultural, and infection-related variables observed in the 20 villages of our 2000 study, but focus on the high prevalence villages. Table 1 summarizes the status of these villages in 2000 including the human infection data, snail data, and the village mean worm burden per mouse determined from the bioassay described in this report. All individuals diagnosed as infected were treated in the winter of 2000–2001.

In China, a bioassay using laboratory mice has been used to detect the presence of cercaria in surface waters in preference to alternative methods because of water turbidity and the sticky nature of Schistosoma japonicum cercariae that makes them difficult to recover from container surfaces or from the tubing of sampling apparatus. The bioassay involves the suspension of a metal screen cage containing five laboratory mice so that the floor of the cage is just below the water surface.2,3 Thus, the tails, paws, and portions of the lower abdomen of the mice are wetted. In these studies, exposure was for five hours per day at mid-day for two days. The mice were then returned to the laboratory and held for six weeks to allow for maturation of the parasite in vivo. The mice that survived the six week period were then killed and dissected, and worms were counted.

Four to six sites were selected per village from among the sites used in the 2000 study. Each of the sites was monitored during the same two-day period at the end of each month from May to October. Air temperature and rainfall data were collected daily in two villages that were selected to be representative of all five. These were Xinmin 7 (representing Xinmin 7 and Shian 5) and Minhe 3 (representing Minhe 3, Xinlong 7, and Tuanjie 2). However, the monitoring stations were not fully functional until June 1 so that only five months of complete village weather data were available. Comprehensive year-long data were obtained from the Xichang Airport Weather Station approximately 15 km from the villages.

Because the worm count data are highly skewed and we wanted to fit a linear regression model, a square-root transformation of the worm counts/mouse was used as an outcome variable. To examine the relative contribution of progressively more local factors (village, location in the village) when accounting for the more regional variables (rain, temperature, month) an analysis of variance sequential (type III) was performed to derive partial R2 values. To account for the possibility of residual correlation of worm counts within a specific cage in a particular month, a random effects model was used.4

RESULTS

Based on data from the Xichang Airport Weather Station, the daily maximum temperatures in 2000 and 2001 over the May–October period were similar, ranging from 15°C to 35°C in May, 20°C to 35°C in July, and 15°C to 30°C in October. Figure 1 shows the daily maximum temperature and Figure 2 shows the weekly running average of the daily precipitation data from the village stations and the weather station at the Xichang Airport Weather Station for the five months in 2001. In general, the weather in the area in both 2000 and 2001 was typical of the region and without notable weather abnormalities. The rainfall data show only minor differences between the stations, except for September in which amounts were markedly higher at the Daxing Station than at the other two stations. The daily maximum temperature was generally lower in Daxing in the summer and higher in the fall than the other two stations.

Table 2 summarizes the bioassay results from 2001 by month, village, and site within a village. As can be seen, mortality in the mice was high for some months in some sites. June in Shian 5, Minhe 3, and Xinlong 7 was the worst month with typically only one of the five mice surviving. Subjectively, the season long data in 2001 are consistent with the July 2000 data for Xinmin 7, Minhe 3, and Tuanjie 2, whereas the data from Shian 5 and, most obviously Xinlong 7, indicate a reduced cercarial risk, a finding of some interest as discussed later in this report. Other obvious features of the data are the site-specific variability in some villages. For example, in Xinmin 7, almost all sites in May have high worm counts as does site 5 in almost all months.

Exploratory analyses were conducted in an attempt to determine what function of the temperature and rainfall time series would be best for use in regression models. Because rainfall is episodic, we investigated the predictive value of various individual days and of three-day to one-week average rainfall on the square root of worms/mouse using a random and fixed effect model. The best predictor was the average rainfall in the week preceding the bioassay experiments. Thus, the one-week average rainfall and temperature were used in subsequent analyses.

The regression results shown in Table 3 were sequentially adjusted for those variables entered already, i.e., adjusted for the effects of those variables in the list above the variable in question. First, the effects of temperature and rainfall were investigated. Neither was a significant predictor at the 5% level and both showed quite small sequential/partial R2 values as shown in Table 3. The next variable entered was month, which also showed a very modest partial R2 value and a P value of 0.44.

The last two variables investigated were village and site within village. Even taking into account the variables related to weather and seasonal changes represented by month, both village and site-within-village were highly significant and together account for approximately half of the total variability in the data set. Thus, these results suggest that village-specific factors contribute considerably more to variability in cercarial risk than short-term weather patterns and seasonal factors within the infection season.

DISCUSSION

Among the variables measured in these villages, the variability in cercarial risk is principally explained by the village itself and the locations within the village at which the bioassay was conducted, rather than the month in which the assay was conducted or short-term fluctuations in temperature and rainfall. However, the bioassay data for October clearly indicate that the infection season was essentially over in four of the five villages, yet month did not account for a significant fraction of the total variability in the data. This may reflect limited power to detect the influence of month, despite the total number of cages of mice deployed in this study.

Because all of the predictor variables used in our analysis account for only approximately 50% of the variability in the bioassay results, and season, temperature, and rainfall explain almost none of this, the precision of estimation of cercarial risk is largely an issue of sample size once sampling locations are fixed. Barring extreme weather events, in this environment the timing of surveys within the main part of the infection season appears to be largely a matter of logistical convenience.

The marked difference between the 2000 and 2001 cercarial risk in Shian 5 and Xinlong 7, and the absence of a similar difference in Xinmin 7, may relate to the degree of connectedness of these villages with upstream sources of cercaria external to these villages. Recall that infected individuals in all five villages were treated in the winter of 2001–2002. This treatment presumably lowered the internally generated miricidial risk to snails in the spring of 2001. The treatment effect appears to have been substantial in Shian 5 and Xinlong 7 and negligible in Xinmin 7. This is consistent with the topographic setting and hydrologic situation of Shian 5 and Xinlong 7, as well as with the proximity of Xinmin 7 to an upstream village known historically to have a high prevalence of infection and which has not been the focus of recent treatment or intervention.

The within and between village variability in cercarial risk also has implications for the relative importance of water contact as a determinant of infection intensity in humans. In our studies in this area in 2000, we did not find exposure time, as determined by a retrospective water contact survey, to be a significant predictor of individual infection intensity, a finding that has been reported by others using a variety of methods of estimating water contact intensity.5–7 Conversely, and as noted earlier, we did find a strong relationship between the bioassay results and both human prevalence and infection intensity. In exploring the reasons underlying these observations in the light of the results presented here, consider the regression equation of the log of individual worm burden, w, predicted by the log of total water contact time, T:

logw=β0+β1logT+ε

In general, the variance of the estimate of the regression parameter β̂1 is proportional to the variance of the model residual error and inversely related to the variance of the covariate T:

var(β^1)σ2(ε)var(logT)

The power to reject the null hypothesis that β1 = 0, i.e., to conclude that water contact is a predictor of worm burden, increases as var(β̂ 1) decreases. Thus, for a given distribution of T, the power to reject the null depends on the variance of the residual error, ϵ.

To identify the component parts of the residual error, we assume infection intensity in humans to be an increasing function of exposure, and define the latter by the product of water contact duration, tij, and the time-weighted cercarial concentration, ci, summed over site, i, and activity, j. That is,

E=ijsjcitij

where sj accounts for differences in water contact intensity by activity, for example, washing clothes versus playing in the water. The effect of differences in exposure due to activity can be incorporated in an activity-adjusted contact time as is often done.6,8,9 With that adjustment, the residual error, ϵ, includes the effects of site-specific cercarial concentration, the fraction of the total contact time spent by the individual at the various sites, differences in individual susceptibility, and measurement errors.

We have calculated rough estimates of the var(log T) and of σ2(ϵ) to allow power calculations specific to the environment we have studied. Using the data presented here to estimate only the component of σ2(ϵ) contributed by the variance of cercarial concentration suggests that we would have marginal power to detect the influence of water contact with a sample size of approximately 200 if all individuals spent equal contact time at each of the sites within a village. However, we know that the individual sites of exposure are highly variable within the village population as has been reported in other settings.10 Furthermore, ascertainment of water contact time is subject to error. These additional sources of variability suggest that, in our studies in 2000, we had low power to detect the effect of water contact time on infection intensity in the absence of site-specific cercarial risk data even if there were no significant differences in human susceptibility to infection at constant exposure. In that regard, we suspect that only in rare and fortuitous circumstances will a practical epidemiologic study design possess the power to detect differences in human susceptibility to schistosome infection without accounting for environmental variability in cercarial risk.

Table 1

Infection status of villages in 2000 and mice and snail populations in 2000 and 2001*

% Mouse prevalenceSnail density% Snails infected
Village year% Human prevalence 2000Human mean EPG 200020002001Mean worms/mouse 20002000200120002001
* Prevalence in mice for 2000 is for July only while that for 2001 is for all six months of the season.
Xinmin 773104867535.437.947.30.61.3
Shian 56891745521.425.037.81.61.1
Minhe 3628463546.513.515.61.00.5
Xinlong 765110541931.720.918.72.31.1
Tuanjie 213118130.31.65.10.00.0
Table 2

Mean number of worms/mouse and (number of mice per assay) by site and month of 2001

VillageSiteMayJuneJulyAugustSeptemberOctoberSite mean
Xinmin 7132.5 (4)96 (4)2.8 (5)8 (4)7.6 (5)1 (5)22.3 (27)
220 (1)3 (2)5.6 (5)0.8 (5)1.2 (5)0 (5)2.8 (23)
373 (1)4.8 (5)6.8 (5)7.2 (5)16.3 (4)1.8 (5)9.6 (25)
42 (5)5.8 (4)5.25 (4)8 (1)11 (5)0 (5)4.9 (24)
579.8 (5)41.5 (4)7.8 (5)259.6 (5)1.2 (5)131 (5)88.4 (29)
Mean39.5 (16)31.7 (19)5.6 (24)68.9 (20)7.1 (24)26.7 (25)28 (128)
Shian 510.4 (5)6 (1)8.6 (3)11 (5)2.4 (5)0 (4)4.4 (23)
20 (1)1 (1)2.8 (5)2.4 (5)2.8 (5)0 (5)1.9 (22)
31.8 (5)0 (1)1 (5)2.8 (5)16.6 (3)0 (5)3.3 (24)
411 (2)0.7 (3)7 (5)0.8 (5)12.4 (5)0 (5)5 (25)
Mean2.5 (13)1.5 (6)4.4 (18)4.3 (20)7.6 (18)0 (19)3.7 (94)
Minhe 318.6 (5)0 (1)0.8 (5)0 (5)0.4 (5)0 (3)2.0 (24)
24.2 (5)26 (1)0.8 (5)2.5 (2)5.6 (5)0 (5)3.7 (23)
316.5 (4)30.5 (4)4.8 (5)4.8 (4)6.3 (4)0 (5)9.8 (26)
436 (1)8 (2)2.8 (5)25 (5)12.5 (4)0 (5)11.0 (22)
53 (4)16 (2)0 (5)22.2 (5)4.4 (5)0.8 (5)7.0 (26)
60.4 (5)0 (1)0 (5)2.8 (5)0 (5)0 (5)0.6 (26)
Mean7.5 (24)17.8 (11)1.5 (30)10.5 (26)4.5 (28)0.1 (28)5.6 (147)
Xinlong 719.2 (5)0 (1)3.4 (5)16.5 (2)0 (4)0.5 (4)4.7 (21)
24.4 (5)0 (1)1.6 (5)0 (1)1 (4)0.8 (5)1.8 (21)
30 (1)0 (5)0 (5)0.4 (5)0 (5)0 (1)0.1 (22)
40 (5)0 (1)0 (5)0 (5)0 (5)0 (1)0 (22)
50 (4)0 (1)0 (1)0 (1)0 (5)0 (4)0 (16)
60 (5)0 (4)0 (5)0 (5)0 (5)0 (5)0 (29)
Mean2.7 (25)0 (13)1.0 (26)1.8 (19)0.1 (28)0.3 (20)1.1 (131)
Tuanjie 217 (2)0 (1)1.2 (5)0 (5)2.6 (3)0 (1)1.6 (17)
20 (5)0 (4)0 (5)0 (1)0 (5)0 (5)0 (25)
30 (1)0 (3)0 (5)0.4 (5)1 (4)0 (4)0.3 (22)
41.2 (5)0 (5)0 (5)0 (5)0.4 (5)0 (3)0.3 (28)
Mean1.5 (13)0 (13)0.3 (20)0.1 (16)0.8 (17)0 (13)0.5 (92)
Table 3

Results of analysis of variance of the contribution of environmental and location variables to variability in cercarial density

VariableSequential R2Sequential P
Rain (past 7 days average)0.00130.47
Temperature (past 7 days average)0.0340.07
Month0.0270.44
Village0.21< 0.0001
Location within village0.320.0001
Figure 1.
Figure 1.

Maximum daily air temperature at three stations in or near the five villages in Sichuan, People’s Republic of China.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 71, 5; 10.4269/ajtmh.2004.71.554

Figure 2.
Figure 2.

One week running average rainfall at three stations in or near the five villages in Sichuan, People’s Republic of China.

Citation: The American Journal of Tropical Medicine and Hygiene Am J Trop Med Hyg 71, 5; 10.4269/ajtmh.2004.71.554

Authors’ addresses: Robert C. Spear, Alan Hubbard, Merrill Birkner, and Justin Remais, Center for Occupational and Environmental Health, School of Public Health, University of California, 140 Warren Hall, Berkeley, CA 94720-7360, Telephone: 510-642-0761, Fax: 510-642-5815, E-mails: spear@berkeley.edu, hubbard@stat.berkeley.edu, mbirkner@uclink.berkeley.edu and jvr@uclink.berkeley.edu. Bo Zhong, Yong Mao, and Dongchuan Qiu, Sichuan Institute of Parasitic Disease, 10 University Road Chengdu 610041, Sichuan, People’s Republic of China, Telephone: 86-28-8544-1258, Fax: 86-28-555-8409, E-mails: qiudc@hotmail.com and zhongbo@hotmail.com.

Acknowledgments: We are indebted to Director Sha Kaiyou and colleagues at the Xichang County Anti-Schistosomiasis Station for their assistance during this study.

Financial support: This work was supported in part by the U.S. National Institute of Allergy and Infectious Disease (grant R01 AI-50612).

REFERENCES

  • 1

    Spear RC, Seto E, Liang S, Birkner M, Hubbard A, Qiu D, Yang C, Zhong B, Xu F, Gu X, Davis G, 2004. Factors influencing the transmission of Schistosoma japonicum in the mountains of Sichuan Province of China. Am J Trop Med Hyg 70 :48–56.

    • Search Google Scholar
    • Export Citation
  • 2

    Zong B, Xiong ME, Zhao LG, Lai YH, Lai J, Chen L, Yin HZ, Sha KY, Zhang Y, Gu XG, 2001. Investigation of water contamination situation in Qionghai Lake area, Xichang, Sichuan. J Pract Parasitic Dis 9 :97–100.

    • Search Google Scholar
    • Export Citation
  • 3

    The Office of Endemic Disease Control M, 2000. Handbook of Schistosomiasis Control. Shanghai: Shanghai Science & Technology Press.

  • 4

    Laird NM, Ware JH, 1982. Random-effects models for longitudinal data. Biometrics 38 :963–974.

  • 5

    Scott JT, Diakhate M, Vereecken K, Fall A, Diop M, Ly A, De Clercq D, de Vlas SJ, Berkvens D, Kestens L, Gryseels B, 2003. Human water contacts patterns in Schistosoma mansoni epidemic foci in northern Senegal change according to age, sex and place of residence, but are not related to intensity of infection. Trop Med Int Health 8 :100–108.

    • Search Google Scholar
    • Export Citation
  • 6

    Gazzinelli A, Bethony J, Fraga LA, LoVerde PT, Correa-Oliveira R, Kloos H, 2001. Exposure to Schistosoma mansoni infection in a rural area of Brazil. I: water contact. Trop Med Int Health 6 :126–135.

    • Search Google Scholar
    • Export Citation
  • 7

    Li Y, Sleigh AC, Williams GM, Ross AG, Forsyth SJ, Tanner M, McManus DP, 2000. Measuring exposure to Schistosoma japonicum in China. III. Activity diaries, snail and human infection, transmission ecology and options for control. Acta Trop 75 :279–289.

    • Search Google Scholar
    • Export Citation
  • 8

    Ross AG, Yuesheng L, Sleigh AC, Williams GM, Hartel GF, Forsyth SJ, Yi L, McManus DP, 1998. Measuring exposure to S. japonicum in China. I. Activity diaries to assess water contact and comparison to other measures. Acta Trop 71 :213–228.

    • Search Google Scholar
    • Export Citation
  • 9

    Friedman JF, Kurtis JD, McGarvey ST, Fraga AL, Silveira A, Pizziolo V, Gazzinelli G, LoVerde P, Correa-Oliveira R, 2001. Comparison of self-reported and observed water contact in an S. mansoni endemic village in Brazil. Acta Trop 78 :251–259.

    • Search Google Scholar
    • Export Citation
  • 10

    Chandiwana SK, Woolhouse ME, 1991. Heterogeneities in water contact patterns and the epidemiology of Schistosoma haematobium. Parasitology 103 :363–370.

    • Search Google Scholar
    • Export Citation
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