Swai_23-29.indd INTRODUCTION Cryptosporidium is an apicomplexan protozoan caus ing intestinal infections and clinical disease in both humans and animals worldwide (Fayer & Ungar 1986; Ramirez, Ward & Sreevatsan 2004). There is evidence for host association of different species of Cryptosporidia. Cryptosporidium hominis (formerly Cryptosporidium parvum genotype 1) is human-spe- cific and maintained in human-to-human transmis- sion cycles, while Cryptosporidium parvum (formerly Cryptosporidium parvum genotype 2) is maintained by a number of different animal reservoir host spe- cies including bovines (Xiao, Fayer, Ryan & Upton 2004). Cryptosporidium parvum also causes disease in humans and is, therefore, a zoonosis that is trans- 23 Onderstepoort Journal of Veterinary Research, 74:23–29 (2007) Prevalence and determinants of Cryptosporidium spp. infection in smallholder dairy cattle in Iringa and Tanga Regions of Tanzania E.S. SWAI1*, N.P. FRENCH2, E.D. KARIMURIBO3, J.L. FITZPATRICK4, M.J. BRYANT5, D.M. KAMBARAGE3 and N.H. OGDEN6 ABSTRACT SWAI, E.S., FRENCH, N.P., KARIMURIBO, E.D., FITZPATRICK, J.L., BRYANT, M.J., KAMBARAGE, D.M. & OGDEN, N.H. 2007. Prevalence and determinants of Cryptosporidium spp. infection in small- holder dairy cattle in Iringa and Tanga Regions of Tanzania. Onderstepoort Journal of Veterinary Research, 74:23–29 The prevalence of Cryptosporidium spp. infection in a cross-sectional study of dairy cattle, from two contrasting dairying regions in Tanzania, were determined by staining smears of faecal samples with the modified Ziehl-Neelsen technique. Of the 1 126 faecal samples screened, 19.7 % were positive for Cryptosporidium spp. The prevalence was lower in Tanga Region than in Iringa Region. The preva- lence of affected farms was 20 % in Tanga and 21 % in Iringa. In both regions, the probability of de- tecting Cryptosporidium oocysts in faeces varied with animal class, but these were not consistent in both regions. In Tanga Region, Cryptosporidium oocysts were significantly more likely to be found in the faeces of milking cows. In Iringa Region, the likelihood that cattle had Cryptosporidium-positive faeces declined with age, and milking cattle were significantly less likely to have Cryptosporidium- positive faeces. In this region, 7 % of cattle were housed within the family house at night, and this was marginally associated with a higher likelihood that animals had Cryptosporidium-positive faeces. Our study suggests that even though herd sizes are small, Cryptosporidium spp. are endemic on many Tanzanian smallholder dairy farms. These protozoa may impact on animal health and production, but also on human health, given the close associations between the cattle and their keepers. Further studies are required to assess these risks in more detail, and understand the epidemiology of Cryptosporidium spp. in this management system. Keywords: Cryptosporidium, dairy cattle, epidemiology, Iringa, prevalence, Tanga, Tanzania * Author to whom correspondence is to be directed. E-mail: emasw@yahoo.co.uk 1 Veterinary Investigation Centre, Box 1068, Arusha, Tanzania 2 Epicentre, Institute of Veterinary, Animal and Biomedical Sciences, College of Sciences, Massey University, Palmerston North, New Zealand 3 Department of Veterinary Medicine and Public Health, Faculty of Veterinary Medicine, Sokoine University of Agriculture, Morogoro, Tanzania 4 Moredun Research Institute, Pentlands Science Park, Bush Loan, Penicuik, UK 5 Department of Agriculture, University of Reading, UK 6 Groupe de Recherche en Épidémiologie des Zoonoses et Santé Publique, Faculté de Médecine Vétérinaire, Université de Montréal, Canada Accepted for publication 15 September 2006—Editor 24 Cryptosporidium spp. infection in smallholder dairy cattle in Tanzania mitted from cattle to humans. For some bovine-as- sociated species, such as C. andersoni, there is no evidence to date for associations with infections or disease in humans (Xiao et al. 2004). Cryptosporidium infections are transmitted by the faeco-oral route, and infections in livestock can be transmitted to hu- mans by direct contact and by faecal contamination of drinking water (Ramirez et al. 2004). The disease is readily transmissible, as oocysts persist for long periods in a suitable environ ment (Castro-Hermida, Gonzalez-Losada & Ares-Mazas 2002), and low numbers of oocysts may produce infection in sus- ceptible hosts (Ramirez et al. 2004). Enteric disease caused by Cryptosporidium spp. is usually self-limit- ing in immunocompetent individuals but debilitating and persistent in those immunocompromised (Rami- rez et al. 2004). Crypto spor idium infection has been termed as an AIDS-defining illness, and its preva- lence amongst AIDS sufferers in Africa is sometimes high (Gatei, Green sill, Ashford, Cuevas, Parry, Cun- liffe, Beeching & Hart 2003). Human infections in Africa have been associated with C. parvum (Tum- wine, Kekitiinwa, Nabukeera, Akiyoshi, Rich, Wid- mer, Feng & Tzipori 2003), which may be of livestock origin because Cryptosporidium spp. infections in livestock appear to be common (Matovelo, Landsverk & Posoda 1984; Esrony, Kambarage, Mtambo, Mu- hairwa & Kusiluka 1996; Mtambo, Sebatwale, Kam- barage, Muhairwa, Maeda, Kusiluka & Kazwala 1997). Smallholder dairy production is now a widespread practice in East Africa, having received support for agricultural development and improved standards of living, as well as being a system that may im- prove human nutrition (Mdoe 1993; Thorpe, Chabari, Maloo, Muinga, Mukhebi, Mullins, Muriethi, Mussu- kuya, Nyambaka, Ole-maki, Otieno, Perry, Rugema & Wekesa 1993). In this study we have investigated the prevalence of Cryptosporidium spp. infections amongst smallholder dairy cattle in two regions of Tanzania. We have also attempted to investigate potential risk factors for infection in these cattle that may point to methods by which infections can be controlled and/or their transmission to humans lim- ited. MATERIALS AND METHODS The study area The study sites are described in detail in Ogden, Swai, Beauchamp, Karimuribo, Fitzpatrick, Bryant, Kambarage & French (2005). Briefly, the study was carried out in two regions of Tanzania, namely the coastal Tanga Region, that lies between 38–39° E and 4–6° S, and the inland highland Iringa Region, lying between 35–36° E and 7–8° S. The study took place in two of the six administrative districts in Iringa Region (Iringa urban and Iringa rural, now Kilolo), and five of the six administrative districts in Tanga Region (Korogwe, Lushoto, Muheza, Pangani and Tanga). Study farm selection Farms in each study region were randomly selected by means of the random number generator in Epi- Info version 6 (CDC, Atlanta, USA), from a sampling frame of 3 001 in Tanga and 500 in Iringa using the databases of the Tanga and Iringa Dairy Devel op- ment Projects. Farms at both study sites were esti- mated to have an average of three to four dairy ani- mals of any age and both sexes, therefore a farm sample size of 200 in each study region was consid- ered necessary to provide between 600 and 800 animals. The sample size in each region was calcu- lated to provide 80 % ability to detect an odds ratio of 2.0, assuming a critical probability of 0.05, and exposure that occurs in 50 % of the population, a prevalence of 20 % in the unexposed group and a design effect of 2.0 (French & Tyrer 1997). Farms recorded to have more than ten animals were ex- cluded from the selection process because farms of this size are not considered as ‘smallholder’ farms (Tanga Dairy Development Programme 1999), al- though a small number of selected farms had more than ten cattle by the time sampling began. Study animals The population of interest consisted of all smallhold- er mixed dairy farmers and dairy cows from the five administrative districts of Tanga and two of the Iringa study regions. A ‘dairy animal’ referred to crosses of Bos taurus cattle (mainly Ayrshire, Friesian and Jer- sey) with the Bos indicus breed, the indigenous Tan- zania short horn zebu (TSHZ) or Boran. The level of exotic blood varied from first to third filial generation (F1, F2 and F3). The animal husbandry practices included both zero and open grazing systems. Over 60 % of the cattle were stall-fed throughout the year. Data collection Data were collected from farms by two separate teams of researchers, one in each region. Faecal sampling and data collection were carried out be- tween January and April 1999. One person in each region collected farm- and animal-level data on a 25 E.S. SWAI et al. structured questionnaire, which was completed on all selected farms on a single visit. The information collected concerned farm and animal events that occurred during 1998, including the numbers of ani- mals that had died or suffered ill health on each farm (not reported here), parasite control, feeding methods and feed types, cattle movements on and off the farm, and grazing and housing practices. The response to many of these questions were investi- gated as explanatory variables in analysis of posi- tivity/negativity for Cryptosporidium spp. infection. These included variables that could be considered as farm-level variables including ‘farm class’ (wheth- er the farm was located in an urban, rural or peri- urban area), frequency of contact with extension officers (rare, moderate or intensive) and farmer at- tendance at a Dairy Development Project training course. Animal housing details, including building, flooring and bedding types, were recorded and used as explanatory variables in statistical analyses. The administrative district in which the farm occurred was also considered as an explanatory variable in the analysis. Animal-level variables included age (centre), sex, breed, filial generation, source (homebred or brought- in), source of brought-in animals (charity gift, dairy development project credit agreement, or cash pur- chase), whether or not the animal had been zero- grazed or allowed to graze at pasture in the three months prior to the onset of the sampling period (October to December 1998), and anthelmintic treat- ments during this period. Factorized ‘classes’ of adult female animals were also created as follows: pregnant cows (all females that have had one calf and were pregnant at the time of the visit), milking cows (all females that were in milk at the time of the visit), and ‘dry cows’ (cows that were not in milk, nor pregnant at the time of the visit). Laboratory analysis of samples During the visit to each farm, a fresh rectal faecal sample was collected from each animal into a ster- ile, airtight, 10 ml plastic tube. Collected faecal sam- ples were labelled and transported in a cool-box to local laboratories prior to despatch in refrigerated containers to Sokoine University of Agriculture in Morogoro. Here, the presence of Cryptosporidium spp. oocysts in faeces samples was detected using the modified Ziehl-Neelsen staining technique as described by Henriksen & Pohlenz (1981). Briefly, faecal smears were prepared on a microscope slide, air dried and fixed with methanol for 5 min. Fixed smears were stained with dilute carbol fuchsin (1:10) for 3–5 min and washed with tap water. Smears were decolourized using acid alcohol, then counterstained with 0.5 % malachite green solution for 1 min. Smear slides were air dried and then examined under the microscope at 400x magnification. Cryptosporidium spp. oocysts appear as pink to red, spherical to ovoid bodies against a green to purple background. Samples were considered positive if at least one morphologically distinct Cryptosporidium spp. oocyst was observed. Statistical analysis Data files of questionnaires and laboratory results were prepared in Epi-Info version 6.04b (CDC, USA). The presence or absence of oocysts in faeces sam- ples was the outcome variable in mixed effects logis- tic regression analyses performed in STATA version 6 for Windows (STATA Corporation, College Station, Texas, USA). The animal- and farm-level variables described above served as explanatory variables. Associations between explanatory variables and the outcome were investigated singly and in multivaria- ble models. In multivariable models, backwards and forwards substitution and elimination were performed to find the most parsimonious model from which no explanatory variables could be removed without sig- nificantly affecting model deviance. Cross tabula- tions and correlations were performed on explana- tory variables to identify highly associated variables that could not be incorporated in the same multi- variable models. In all models, the farm identifica- tion (ID) number was considered a random effect to account for clustering of animals by farm, and the level of significance was P < 0.05 throughout. Sep- arate statistical analyses were performed for the data from the two regions because previous studies have indicated that parasite ecology and epidemiol- ogy may be very different in the two regions (Ogden et al. 2005; Swai, French, Karimuribo, Fitzpatrick, Bryant, Brown & Ogden 2005). RESULTS Farm response rate All selected 200 farms from each of Tanga and Iringa Regions were visited during the period of January to April 1999. In Tanga, a total of 697 animals kept on 185 farms (92.5 % of the sample) were examined and sampled. Fifteen farms (7.5 %) had no animals during the actual survey period. In Iringa, a total of 698 animals from 195 farms (97.5 % of the sample) were examined and sampled. Three farms (1.5 %) had no animals and animals on two farms (1 %) could 26 Cryptosporidium spp. infection in smallholder dairy cattle in Tanzania TABLE 1 The proportions of cattle in each category of each variable investigated during the study Variable Categories No. of animals (%) Iringa Tanga Animal-level variables Sex Male Female 182 (26) 516 (74) 146 (21) 551 (79) Source of animal Homebred Brought-in 406 (58) 292 (42) 436 (63) 261 (37) Filial generation F1 F2 F3 350 (50) 347 (49.9) 1 (0.1) 217 (31) 459 (66) 21 (3) Adult cow ‘class’ Milking cows Dry cows Pregnant cows 158 (22.6) 60 (8.6) 65 (9.3) 137 (20) 50 (7) 86 (12.3) Breed Ayrshire cross Friesian cross Jersey cross Simmental cross Sahiwal cross TSHZ cross Boran cross 403 (58) 305 (44) 1 (0.1) 0 0 150 (22) 549 (78) 169 (24) 604 (86) 12 (2) 5 (1) 12 (2) 541 (77) 121 (17) Age < 3 years 3 to < 6 years > 6 years 440 (63) 165 (24) 93 (13) 396 (57) 214 (31) 87 (12) Grazing history in last 3 months of 1998 Zero grazing Semi/free grazing 423 (61) 275 (39) 626 (90) 71 (10) Anthelmintic treatment in 1998 Yes No 614 (88) 84 (12) 313 (45) 384 (55) Farm-level variables Farm classification Peri-urban Urban Rural 109 (16) 391 (56) 198 (28) 117 (17) 318 (46) 262 (37) Housing details Cowshed with roof Cowshed no roof Traditional kraal or boma 531 (76.1) 80 (11.4) 87 (12.5) 644 (92.4) 39 (5.6) 14 (2) Sleeping area Cubicle in cow shed Cow shed floor Family house 398 (57) 247 (35.3) 53 (7.6) 374 (53.6) 315 (45.2) 8 (1.14) Floor/bedding type Concrete Left over forage Dried grass Hardcore Soil Wood 335 (47.9) 0 (0) 23 (3.3) 67 (9.5) 201 (28.7) 72 (10.3) 402 (57.6) 69 (9.9) 16 (2.3) 38 (5.4) 168 (24.1) 4 (0.6) Frequency of extension officer contact Rare Moderate Intensive 15 (2) 596 (85) 87 (13) 6 (0.8) 659 (94.5) 32 (4.7) District (Iringa) Iringa urban Iringa rural (Kilolo) 461 (66) 237 (34) NA District (Tanga) Tanga Muheza Pangani Korogwe Lushoto NA 341 (48.8) 185 (26.5) 26 (4) 64 (9) 81 (12) NA = Not applicable 27 E.S. SWAI et al. not sampled because the owner could not be traced. The number of animals examined per herd ranged from 1–13 animals. The age range of animals exam- ined varied from 1 day to 13 years. The characteris- tics of the sample of cattle in each region are de- tailed in Table 1. Prevalence of Cryptosporidium spp. oocysts in faeces samples Results were available from 444 of the 697 animals sampled in Tanga, and 682 of the 698 animals sam- pled in Iringa. The missing results arose due to loss of labels during transport to laboratories. Of the 1 126 faecal samples screened in both Tanga and Iringa, 222 (19.7 %) were positive for Cyptosporidium spp oocysts. The prevalence amongst study ani- mals investigated was higher in Iringa than in Tanga (Table 2). In Tanga Region, the highest prevalence was ob- served in cattle in Tanga, Korogwe, Muheza and least in Lushoto and none in Pangani districts (Table 2), although these differences were not significant (likelihood ratio statistic [LHR] = 4.89, df = 4, P > 0.1). In Tanga Region, on 37 of the 185 farms sam- pled (20 %, 95 % confidence interval [CI] = 14.5– 26.5) at least one animal tested positive for Crypto- sporidium infection. In Iringa Region, 41 of 195 farms (21 %, CI = 15.5–27.4) had at least one animal pos- itive. In Iringa Region, two factors remained significant in the multivariable model. First, the likelihood that faecal samples were positive decreased significant- ly with animal age (coefficient = –0.009, SE = 0.004, P = 0.017). Second, faecal samples from milking cows were significantly less likely to be positive than those of other classes of cattle (odds ratio [OR] = 0.56, CI = 0.32–0.97, P = 0.038). Faecal samples from animals that were kept in the family house at night were positive more often than samples from animals housed in other buildings but the difference was not significant (OR = 1.75, CI = 0.92–3.34, P = 0.09). In Tanga, milking cows were significantly more likely to have positive faeces samples than other animal classes (OR = 2.19, CI = 1.08–4.39, P = 0.028). The random effect of farm ID number was negligible in Tanga Region (coefficient = 0.001, SE = 0.735), but strong in Iringa Region (coefficient = 0.472, SE = 0.237). DISCUSSION In this study, there was evidence that Cryptosporidium spp. are endemic on smallholder dairy farms in two different regions of Tanzania. The detected preva- lence of infection in the cattle was intermediate in comparison with that observed in other studies in Tan zania, from 5.3 % (Mtambo et al. 1997) up to 62 % (Lema 1990; Esrony et al. 1996). It was gener- ally lower than observed in studies in Europe for in- stance up to 36 % in dairy cattle in Germany (Joa- chim, Krull, Schwarzkopf & Daugschies 2003) and 80 % in calves Britain (Scott, Smith, Mtambo & Gibbs 1995). While the method of detection used in the present study is a rapid screening method, there was no attempt to concentrate faecal oocysts and the method has low sensitivity of detection compared to other methods (Gobet, Buisson, Vagner, Naciri, Grap pin, Comparot, Harly, Aubert, Varga, Camer- lynck & Bonnin 1997). Furthermore, our cross-sec- tional study would not capture the potential for sea- sonal variations in prevalence that, in studies in Europe, may be considerable (Huetink, Van der Giessen, Noordhuizen & Ploeger 2001). TABLE 2 The prevalence of Cryptosporidium spp. in dairy cattle by regions and districts in Iringa and Tanga. 95 % CI = confidence intervals adjusted for the farm effect District Number of animals positive % positive (95% CI) Iringa Region Iringa rural Iringa urban All 43/201 138/481 181/682 21.3 (18.4–25.0) 28.6 (26.5–30.9) 26.5 (24.7–28.4) Tanga Region Tanga Muheza Korogwe Lushoto Pangani All 24/228 12/134 4/38 1/33 0/11 41/444 10.5 (8.6–13.0) 8.9 (5.6–12.2) 10.5 (8.6–13.0) 3.03 (0.07–7.7) 0 (0–28.5) 9.3 (7.7–11.0) 28 Cryptosporidium spp. infection in smallholder dairy cattle in Tanzania Our prevalence estimates are likely, therefore, to be lower than the true prevalence in the cattle, but it could be expected that features of cattle demogra- phy and management on smallholder dairy farms are not conducive to the maintenance of endemic cycles of Cryptosporidium spp. by the cattle alone. These include the low number of cattle on individual farms, often consisting of just one or two animals, low rates of reproduction amongst the cattle, and a low rate of production of Cryptosporidium-naïve and susceptible calves (French, Tyrer & Hirst 2001). The mostly zero-grazed management may reduce the potential for farm-to-farm transmission. Despite these features, which could all result in fade-out of infec- tious diseases (Anderson & May 1992), Crypto spor- idium spp. were detected in faeces of the cattle in nearly all the administrative districts in both regions studied. There was evidence of geographic variations in the prevalence, and possibly the dynamics of infection. First, the detected prevalence of infection was high- er in cattle in Iringa Region than in Tanga Region. This is consistent with unpublished observations for prevalence of infection in other species in Tanzania (dogs, pigs and cattle in other management sys- tems) in which prevalence is generally higher in re- gions that have a cooler climate, suggesting that the high ambient temperature and humidity in coastal Tanga may be sub-optimal for survival of Cryptosporidium spp. outside the host. Second, the risk factors for infection varied between the two re- gions. In Iringa Region, younger animals were more likely to be positive, and milking adult cows were particularly unlikely to be detectably infected. In Tanga Region, there was no association of detected infection with age, but milking adult cows were par- ticularly likely to be positive. In addition, the farm effect in Iringa Region was high suggesting that un- measured management characteristics of individual farms had a considerable effect on Cryptosporidium spp. epidemiology, which was not the case in Tanga Region. This may also suggest that important fea- tures of the epidemiology of these infections may differ in the two regions, which could also be associ- ated with differences in climate between the regions. In a study of Cryptosporidium spp. dynamics in cattle of farms in the USA, however, infections in younger animals were associated with the zoonotic C. par- vum, while infections in older animals were associ- ated with non-zoonotic species or genotypes (San- tin, Trout, Xiao, Zhou, Greiner & Fayer 2004). Thus our observed regional differences in the epidemiol- ogy of Cryptosporidium spp. could possibly signify differences in the risk of human infections. Our study indicated that smallholder dairy farmers and their families often live in close proximity to their cattle, even within the family home. The probability of transmission of zoonotic Cryptosporidium spp. from cattle to humans on these farms may, there- fore, be high. Farmers keep their cattle within the family home at night to prevent the theft of these val- uable commodities (Karimuribo, personal communi- cation 2002). It is not clear, however, why the de- tected prevalence of infection in cattle that are brought into the home at night was particularly high; this warrants further investigation. In this study we have demonstrated the presence of Cryptosprodium spp. infection in smallholder dairy cattle in two regions of Tanzania. The epidemiology of infection may differ in different regions with poten- tial consequences for human health. Some cattle- keeping practices are likely to enhance direct bovine- to-human transmission. Further studies are needed to understand the dynamics of transmission cycles and the genetic diversity of Cryptosporidium spp. on the farms, and to identify and if possible alter man- agement practices that are risk factors for human infections. ACKNOWLEDGEMENTS This study was funded by the UK Department for International Development. We thank all the farm- ers who participated, and the veterinarians and staff of the Tanga and Southern Highlands Dairy Devel- op ment Programmes for their very considerable support and assistance. Thanks are extended to the Director of Veterinary Service, Tanzania for permis- sion to publish this work. REFERENCES ANDERSON, R.M. & MAY, R.M. 1992. Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press. CASTRO-HERMIDA, J.A., GONZALEZ-LOSADA, Y.A. & ARES- MAZAS, E. 2002. Prevalence of and risk factors involved in the spread of neonatal bovine cryptosporidiosis in Galicia (NW Spain). Veterinary Parasitology, 106:1–10. ESRONY, K., KAMBARAGE, D.M., MTAMBO, M.A.A., MU HAIR- WA, A.P. & KUSILUKA, L.J.M. 1996. Intestinal protozoan parasites of pigs under different management systems in Morogoro, Tanzania. Journal of Applied Animal Research, 10:25–31. FAYER, R. & UNGAR, B.L. 1986. Cryptosporodium spp. and cryptosporodosis. Microbiology Review, 50:458–483. FRENCH, N.P. & TYRER, J. 1997. Birth and death of cattle on small-scale dairy farms in Zimbabwe. Society of Veterinary Epidemiology and Preventive Medicine, Chester (poster pres- entation). 29 E.S. SWAI et al. FRENCH, N.P., TYRER, J. & HIRST, W.M. 2001. Smallholder dairy farming in the Chikwaka communal land, Zimbabwe: birth, death and demographic trends. Preventive Veterinary Medicine, 48:101–112. GATEI, W., GREENSILL, J., ASHFORD, R.W., CUEVAS, L.E., PARRY, C.M., CUNLIFFE, N.A., BEECHING, N.J. & HART, C.A. 2003. Molecular analysis of the 18S rRNA gene of Cryptosporidium parasites from patients with or without hu- man immunodeficiency virus infections living in Kenya, Ma- lawi, Brazil, the United Kingdom, and Vietnam. Journal of Clinical Microbiology, 41:1458–1462. GOBET, P., BUISSON, J.C., VAGNER, O., NACIRI, M., GRAP- PIN, M., COMPAROT, S., HARLY, G., AUBERT, D., VARGA, I., CAMERLYNCK, P. & BONNIN, A. 1997. Detection of Cryptosporidium parvum DNA in formed human feces by a sensitive PCR-based assay including uracil-N-glycosylase inactivation. Journal of Clinical Microbiology, 35:254–256. HENRIKSEN, S.A. & POHLENZ, J.F.L.1981. Staining of Crypto- sporodia by modified Ziehl-Neelsen technique: a brief com- munication. Acta Veterinaria Scandinavica, 25:322–326. HUETINK, R.E., VAN DER GIESSEN, J.W., NOORDHUIZEN, J.P. & PLOEGER, H.W. 2001. Epidemiology of Cryptospor- idium spp. and Giardia duodenalis on a dairy farm. Veterinary Parasitology, 102:53–67. JOACHIM, A., KRULL, T., SCHWARZKOPF, J. & DAUGSCHIES, A. 2003. Prevalence and control of bovine cryptosporidiosis in German dairy herds. Veterinary Parasitology, 112:277– 288. LEMA, B. 1990. Study of aetiologies of neonatal calf diarrhoea in selected farms in Tanzania. Ph.D. thesis, University of Gies- sen, Germany. MATOVELO, J.A., LANDSVERK, T. & POSODA, G.A. 1984. Cryptosporidosis in Tanzania goat kids, scanning and elec- tron microscopic observations. Acta Veterinaria Scandinavica, 2:322–326. MDOE, N. 1993. Small holder dairy production and marketing of milk in Hai District, Tanzania. Ph.D. thesis, University of Read ing, UK. MTAMBO, M.M., SEBATWALE, J.B., KAMBARAGE, D.M., MU- HAIRWA, A.P., MAEDA, G.E., KUSILUKA, L.J. & KAZWALA, R.R. 1997. Prevalence of Cryptosporidium spp. oocysts in cattle and wildlife in Morogoro region, Tanzania. Preventive Veterinary Medicine, 31:185–190. OGDEN, N.H., SWAI, E., BEAUCHAMP, G., KARIMURIBO, E., FITZPATRICK, J.L. BRYANT, M.J., KAMBARAGE, D. & FRENCH, N.P. 2005. Risk factors for tick attachment to small-holder dairy cattle in Tanzania. Preventive Veterinary Medicine, 67:157–170. RAMIREZ, N.E., WARD, L.A. & SREEVATSAN, S. 2004. A re- view of the biology and epidemiology of cryptosporidiosis in humans and animals. Microbes Infection, 6:773–785. SANTIN, M., TROUT, J.M., XIAO, L., ZHOU, L., GREINER, E. & FAYER, R. 2004. Prevalence and age-related variation of Cryptosporidium species and genotypes in dairy calves. Vet- erinary Parasitology, 122:103–117. SCOTT, C.A., SMITH, H.V., MTAMBO, M.M.A. & GIBBS, H.A. 1995. An epidemiological study of Cryptosporidium parvum in two herds of adult beef cattle. Veterinary Parasitology, 57: 277–289. SWAI, E.S., FRENCH, N.P., KARIMURIBO, E.D., FITZPATRICK, J.L., BRYANT, M.J., BROWN, P.E. & OGDEN, N.H. 2005. Spatial and management factors associated with exposure of smallholder dairy cattle in Tanzania to tick-borne patho- gens. International Journal of Parasitology, 35:1085–96. TANGA DAIRY DEVELOPMENT PROGRAMME 1999. Annual progress report, Tanga. THORPE, W., CHABARI, F., MALOO, S.H., MUINGA, R.W., MU- KHEBI, A., MULLINS, G., MURIETHI, J., MUSSUKUYA, E., NYAMBAKA, R., OLE-MAKI, M., OTIENO, L., PERRY, B., RUGEMA, E. & WEKESA, E. 1993. Smallholder dairy cattle production in coastal Kenya: resource base assessment and constraint identification. Proceedings of the Meeting on Animal Production in Developing Countries, Ashford, UK, Sept 2–4, 1991, British Society of Animal Production, Peni- cuik, Scotland, UK: 167–168. TUMWINE, J.K., KEKITIINWA, A., NABUKEERA, N., AKIYOSHI, D.E., RICH, S.M., WIDMER, G., FENG, X. & TZIPORI, S. 2003. Cryptosporidium parvum in children with diarrhea in Mulago Hospital, Kampala, Uganda. American Journal of Tropical Medicine Hygiene, 68:710–715. XIAO, L., FAYER, R., RYAN, U. & UPTON, S.J. 2004. Crypto- sporidium taxonomy: recent advances and implications for public health. Clinical Microbiology Review, 17:72–97.