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Estimation of the true prevalence of inaccurate artificial inseminations in Irish milk recording dairy cows using a
E T Kelly1, C G McAloon1, M A Crowe1
1School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Inaccurate artificial insemination (IAI) refers to an artificial insemination (AI) that is performed when a cow is not in oestrus. IAIs have economic impacts on the dairy industry through of semen wastage or iatrogenic pregnancy loss. However, few studies have quantified the prevalence of IAIs in a population. The primary objective of this prospective study was to estimate the cow-level true prevalence of IAIs in Irish milk recording dairy herds using a latent class model with a Bayesian framework. Milk samples were collected at a milk recording laboratory from 576 dairy cows in 125 herds who had received an AI on the same day they were sampled for routine milk constituent analysis. Milk progesterone (MP4) analysis was conducted on these samples using radioimmunoassay to determine the progesterone concentration. Fertility data (i.e., subsequent calving date) was retrospectively obtained from the Irish National Cattle Breeding Federation for milk sampled cows and an apparent conception (AC) to the sample AI was determined based on an estimated gestational range of 270-290 days. Both tests (MP4 and AC) were used in a latent class model to estimate the true prevalence of IAI. For the MP4 test, a concentration of ≥ 5 ng/mL in whole milk was deemed to be test positive while for the AC test, a cow that did not conceive to the sampled AI was deemed test positive. Prior information for prevalence of IAI was obtained from a literature review while MP4 sensitivity (Se) and specificity (Sp) were obtained from expert opinion. Non-informative priors were used for the Se and Sp of the AC test. Posterior inferences (median and 95 % Bayesian probability intervals; BPI) were obtained using the 'rjags' package in the R statistical software. In the final model, median cow-level true prevalence of IAI was 4.4 % (BPI; 1.7-9.0 %). Median Se and Sp estimates for MP4, were 83.0 % (BPI; 65.0-96.2 %) and were 97.4 % (BPI; 94.6-99.6 %), respectively. Median Se and Sp estimates for AC, were 64.8 % (BPI; 44.5-88.6 %) and 49.8 % (BPI; 45.3-54.1 %), respectively. The present study estimates that the overall cow-level true prevalence of IAI in Irish dairy cows is relatively low. This is the first study to report the cow-level true prevalence of IAI using a Bayesian latent class model.
Inaccurate artificial insemination (IAI) refers to an artificial insemination (AI) that is performed when a cow is not in oestrus. IAIs have economic impacts on the dairy industry through of semen wastage or iatrogenic pregnancy loss. However, few studies have quantified the prevalence of IAIs in a population. The primary objective of this prospective study was to estimate the cow-level true prevalence of IAIs in Irish milk recording dairy herds using a latent class model with a Bayesian framework. Milk samples were collected at a milk recording laboratory from 576 dairy cows in 125 herds who had received an AI on the same day they were sampled for routine milk constituent analysis. Milk progesterone (MP4) analysis was conducted on these samples using radioimmunoassay to determine the progesterone concentration. Fertility data (i.e., subsequent calving date) was retrospectively obtained from the Irish National Cattle Breeding Federation for milk sampled cows and an apparent conception (AC) to the sample AI was determined based on an estimated gestational range of 270-290 days. Both tests (MP4 and AC) were used in a latent class model to estimate the true prevalence of IAI. For the MP4 test, a concentration of ≥ 5 ng/mL in whole milk was deemed to be test positive while for the AC test, a cow that did not conceive to the sampled AI was deemed test positive. Prior information for prevalence of IAI was obtained from a literature review while MP4 sensitivity (Se) and specificity (Sp) were obtained from expert opinion. Non-informative priors were used for the Se and Sp of the AC test. Posterior inferences (median and 95 % Bayesian probability intervals; BPI) were obtained using the 'rjags' package in the R statistical software. In the final model, median cow-level true prevalence of IAI was 4.4 % (BPI; 1.7-9.0 %). Median Se and Sp estimates for MP4, were 83.0 % (BPI; 65.0-96.2 %) and were 97.4 % (BPI; 94.6-99.6 %), respectively. Median Se and Sp estimates for AC, were 64.8 % (BPI; 44.5-88.6 %) and 49.8 % (BPI; 45.3-54.1 %), respectively. The present study estimates that the overall cow-level true prevalence of IAI in Irish dairy cows is relatively low. This is the first study to report the cow-level true prevalence of IAI using a Bayesian latent class model.
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