Meta-analysis of the parasitic phase traits of Haemonchus contortus infection in sheep.

BACKGROUND: The parasitic nematode Haemonchus contortus shows highly variable life history traits. This highlights the need to have an average estimate and a quantification of the variation around it to calibrate epidemiological models.

METHODS: This paper aimed to quantify the main life history traits of H. contortus and to identify explanatory factors affecting these traits using a powerful method based on a systematic review and meta-analysis of current literature. The life history traits considered are: (i) the establishment rate of ingested larvae; (ii) the adult mortality rate; (iii) the fertility (i.e. the number of eggs laid/female/day); and (iv) fecundity of female worms (i.e. the number of eggs per gram of faeces).

RESULTS: A total of 37 papers that report single experimental infection with H. contortus in sheep and published from 1960 to 2015, were reviewed and collated in this meta-analysis. This encompassed 115 experiments on 982 animals. Each trait was analysed using a linear model weighted by its inverse variance. The average (± SE) larval establishment rate was 0.24 ± 0.02, which decreased as a function of the infection dose and host age. An average adult mortality rate of 0.021 ± 0.002) was estimated from the literature. This trait varied as a function of animal age, breed and protective response due to prior exposure to the parasite. Average female fertility was 1295.9 ± 280.4 eggs/female/day and decreased in resistant breeds and previously infected hosts. Average faecal egg count at necropsy was 908.5 ± 487.1 eggs per gram of faeces and varied as a function of infection duration and host resistance. The average sex ratio of H. contortus was 0.51 ± 0.006.

CONCLUSION: This work is the first systematic review to summarise the available information on the parasitic phase of H. contortus in sheep. The results of the meta-analysis provide robust estimates of life history traits for parametrization of epidemiological models, their expected variation according to experimental factors, and provides correlations between these.