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Evaluation of the performance of register data as indicators for dairy herds with high lameness prevalence

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Abstract

Background

The modern dairy industry routinely generates data on production and disease. Therefore, the use of these cheap and at times even “free” data to predict a given state of welfare in a cost-effective manner is evaluated in the present study. Such register data could potentially be used in the identification of herds at risk of having animal welfare problems. The present study evaluated the diagnostic performance of four routinely registered indicators for identifying herds with high lameness prevalence among 40 Danish dairy herds. Indicators were extracted as within-herd annual means for a one-year period for cow mortality, bulk milk somatic cell count, proportion of lean cows at slaughter and the standard deviation (SD) of age at first calving. The target condition “high lameness prevalence” was defined as a within-herd prevalence of lame cows of  ≥ 16% (third quartile). Diagnostic performance was evaluated by constructing and analysing Receiver Operating Characteristic curves and their area under the curve (AUC) for single indicators and indicator combinations. Sensitivity (Se) and specificity (Sp) of the indicators were assessed at the optimal cut-off based on data and compared to a set of predefined cut-off levels (national annual means or 90-percentile).

Results

Cow mortality had the highest AUC (0.76), while adding the three other indicators to the model did not yield significant increase in AUC. Cow mortality and SD of age at first calving had highest Se (100%, 95% confidence interval (CI): 72–100%), while highest Sp was found for the proportion of lean cows at slaughter (83%, 95% CI: 66–93%). The highest differential positive rate (DPR = 0.53) optimizing both Se and Sp was found for cow mortality. Optimal cut-off points were lower than the presently used pre-defined cut-offs.

Conclusions

The selected register-based indicators proved to be able to identify herds with high lameness prevalences. Optimized cut-offs improved the predictive ability and should therefore be preferred in official control schemes.

Background

Increased public focus on animal welfare has led to the implementation of various welfare assurance schemes for commercial use, especially for cattle and pig herds. Parallel to these industry-based welfare schemes, authorities also conduct animal welfare controls. In order to obtain valid national estimates of the national welfare level of e.g. dairy herds, on-farm assessments would have to be performed in all herds. Such assessments would require direct animal-based measures of the clinical and behavioural state of animals, as these are perceived to be closest to the true state of welfare. However, such data are very costly to retrieve due to investigator training and calibration and the actual time needed for herd visits. Hence, a more targeted approach is needed. This approach could be used for pre-screening of herds at risk of having welfare problems, subsequently reducing the number of herds visited. For this purpose, register data could be utilised, as direct consequences of clinical manifestations are reflected in e.g. milk production and reproductive results available through register data. The Danish welfare control programme uses register-based indicators to identify livestock herds at risk of welfare problems based on a set of risk parameters from the national databases. This initial screening is followed by a control visit by the authorities in selected herds. The initial screening is based on certain cut-offs for the given parameters, but there is a need to investigate how sensitive these cut-offs are and how optimized cut-offs would perform instead, not only for the official selection of herds but also in other welfare aspects such as commercial welfare assurance schemes.

Within modern livestock production, vast amounts of data are generated and routinely recorded in databases. Data like disease recordings and production results are of great value for epidemiological research and have traditionally been used in e.g. investigating risk factors. Over the past decades, non-specific routine registrations (i.e. secondary data) have also become of interest in so-called syndromic surveillance schemes [1,2,3,4,5]. These surveillance schemes have been used for different purposes in different species such as disease outbreak prediction in cattle [6, 7], pigs [8] and horses [9]. Likewise, various register-based indicators such as treatment records have previously been used to predict more distinct clinical manifestations in dairy cattle. Milk production data, e.g. milk yield, bulk milk somatic cell count (BMSCC) and fat/protein ratio have been studied for associations with several outcome measures like mortality [10, 11], lameness related diseases [12, 13] and metabolic disorders [14, 15]. A review by deVries et al. [16] investigating associations between register-based variables and welfare indicators from the Welfare Quality® assessment protocol found contradicting reports on the associations between variables related to productivity and welfare indicators. That led to the conclusion, that even though numerous associations between register-based indicators and direct clinical and behavioural conditions exist, the ability of register-based indicators to estimate animal welfare is not fully understood. These indicators are often limited to uncover only a few aspects of the multi-dimensional complex of animal welfare.

In Denmark, all livestock herds are required to register birth, death and movement of animals to the Central Husbandry Register (CHR). All treatments with prescription drugs performed by either a veterinarian or the farmers must be reported to the national database VetStat. Additionally, an industry database, the Danish Cattle Database (DCD), compiles data from the official databases, the milk recording scheme, breeding associations, laboratory findings and abattoirs. These data have been used to assess animal welfare in dairy herds with different welfare definitions. Otten et al. [17] investigated register-based indicators for violations of animal welfare legislation as would be detected by official animal welfare control. The reported increasing probability of violations with increasing variation (standard deviation [SD]) in milk yield between cows in first lactation if herds had a BMSCC > 250,000 cells/mL and less than 25 veterinary treatments/100 cow years, emphasize the association between management and animal welfare. Animal welfare experts often refer to lameness as ‘the most important’ animal welfare measure [18,19,20] in modern dairy production. Not only due to the painful nature of most locomotor disorders [21,22,23] but also due to the subsequent impact of decreased fertility and longevity [24,25,26,27]. Hence, the use of register data as a screening tool might prove valuable for monitoring certain welfare aspects such as direct clinical measures like lameness.

In order to explore the opportunities for predicting a direct physiological state on a given day, the present study seeks to combine data from different sources in order to see if register data are able to predict high lameness prevalences. Therefore, the objectives of the present study were to investigate the predictive ability of different register data indicators to identify dairy herds with high lameness prevalence and compare presently predefined cut-offs with optimized cut-offs of the given indicators.

Methods

For the purpose of this paper, lameness data gathered in 40 Danish dairy herds in a previous study by Thomsen et al. [28] were used to establish the target condition of high within-herd lameness prevalence (for details on herd and cow sampling please see [28]).

Clinical protocol—target condition

The lameness score used was a five point ordinal scale described in [25] ranging from a score 1 for non-lame cows to a score 5 for severely lame cows. In the present study cows with a score 4 or 5 were classified as “lame”. The overall mean within-herd lameness prevalence across all herds was 12.9 ± 9.88% (SD) with a median of 11%. The obtained mean herd level prevalence was dichotomized using the third quartile as a cut-off. This led to a classification of herds into either having a low lameness level for herds having a mean prevalence of lame cows < 16%; or a high lameness level for herds with a prevalence ≥ 16%.

Register-based indicators

A literature review on register-based indicators for impaired animal welfare in dairy cows was performed. Findings in previous Nordic studies [29,30,31] lead to the choice of four indicators representing different aspects of a dairy cow’s lifespan i.e. from entry to exit of the milk production period. The indicators cow mortality, BMSCC, proportion of lean cows at slaughter defined as cows with fat score 1 according to the EU Beef Carcase Classification Scheme and SD of age at first calving were chosen for the present analysis and were extracted from the DCD for the year 2004 as annual means per 100 cow years.

Indicators were assessed in two different models: a data-driven model evaluating indicators measured as continuous variables and a predefined cut-off model based on dichotomization of indicators based on predefined cut-offs. National means for the year 2004 (according to the Danish Knowledge Centre for Agriculture and the dairy industry) were used as predefined cut-offs for the indicators cow mortality, BMSCC and SD of age at first calving. The cut-off for the indicator lean cows at slaughter was not based on the national mean, as this was as low as 15% compared to the variable mean of 24% in the present sample. Hence, the cut-off was chosen to reflect the national 90th-percentile.

Statistical analyses

All associations between the outcome (low or high lameness) and the indicators as continuous variables were assessed in univariable and multivariable screening in logistic models using the glm function in R [32]. Additionally, indicators were also assessed as dichotomized variables according to the predefined cut-offs by testing their associations with low or high lameness prevalence using McNemar tests. Correlations between explanatory variables were evaluated by means of Spearman´s correlation coefficient.

Sensitivity (Se) and specificity (Sp) estimates and their 95% confidence intervals (95% CI) at the optimal cut-off points were determined. Se was defined as the fraction of herds with an indicator level above the given cut-off among herds with high lameness level, i.e. with a prevalence of lame cows ≥ 16%. Sp was defined as the fraction of herds with an indicator level below cut-off among herds with low lameness level (< 16%).

The optimal cut-offs maximizing the differential positive rates (DPR = Se + Sp−1) of each indicator were identified by analysing Receiver Operating Characteristic curves (ROC). The predictive ability was quantified by assessing the area under the curve (AUC) [33]. All ROC analyses were made using the R-package pROC [34]. All indicators were assessed individually followed by an assessment of different indicator combinations. Model selection was based on comparing the Akaike Information Criterion (AIC) (significance level of P < 0.05) of the given models. Finally, the differences in AUC between models were assessed using the rcomp function.

Results

A descriptive summary of the indicators is given in Table 1. Amongst the continuous indicators only cow mortality (P = 0.04) and BMSCC (P = 0.05) showed significant associations with high lameness prevalences. The McNemar tests revealed significant associations between high lameness prevalences and mortality (P = 0.002), BMSCC (P < 0.001) and lean cows at slaughter (P < 0.001) while SD of age at first calving was not significantly associated with high lameness prevalences (P = 0.45). Mortality was significantly correlated to BMSCC (P = 0.03) and SD of age at first calving (P = 0.05).

Table 1 Descriptive statistics for four register-based indicators for discriminating between lameness prevalences ≥ 16% (High, n = 10) and < 16% (Low, n = 30) in 40 Danish dairy herds

Cut-offs maximizing the DPR are given in Table 2 together with the predefined cut-offs. The cut-offs for the indicators identified by the data-driven optimization approach were lower than the predefined cut-off except for the proportion of lean cows at slaughter.

Table 2 Comparison of cut-offs used in the dichotomization of indicators of high lameness prevalence in 40 Danish dairy herds

Individual evaluation of the continuous indicators showed cow mortality having the highest AUC of 0.76, followed by BMSCC with an AUC of 0.73 (Table 3). Furthermore, only the AUC´s for cow mortality and BMSCC were significantly different from 0.5 (the random AUC of a test with no information). Comparison of the classification of case herds based on either predefined or data-driven cut-offs for maximizing the AUC, Se and Sp are shown in Fig. 1. In practice, this would mean that if herds were to be selected for animal welfare control based on the predefined cut-offs, four truly positive herds would be overlooked, while the number of false positives would be lower compared to data-driven approach.

Table 3 Model area under the curve (AUC) compared to the area under a random and non-informative receiver operating characteristic (ROC) curve (AUC = 0.5)
Fig. 1
figure1

Receiver operating characteristic (ROC) plot of the indicator cow mortality quantifying the diagnostic potential to an area under the curve (AUC) of 76% (dashed line) compared to the full model including all four explanatory variables with an AUC of 78% (grey line)

Highest Se was found for cow mortality (100%, 95% CI: 69–100%) and lean cows (90%, 95% CI: 56–100%), but with fairly low corresponding Sp (mortality 67%, 95% CI: 47–83%; lean cows 20%, 95% CI: 8–39%) (Table 3). Highest Sp was found for BMSCC and mortality (67%, 95% CI: 47–83%). Models evaluating indicators based on predefined cut-offs and the combination of cow mortality, BMSCC and lean cows achieved the highest AUC (0.71). Model parameters are shown in Table 4. Combining the variables yielded only a slight and non-significant improvement of the AUC (0.78) (Table 5 and Fig. 2).

Table 4 Parameter estimates, standard error (SE), P-values, Akaike Information Criteria (AIC) for univariable logistic regression models assessing the associations between high lameness prevalence and four indicators at predefined and optimized cut-offs
Table 5 Results of stepwise addition of indicators given by the area under the curve (AUC), model fit by Akaike Information Criterion (AIC) and test for significant increase in model AUC (P-value) compared to the random curve
Fig. 2
figure2

Plots of indicators (cow mortality and bulk milk somatic cell count) with predefined cut-offs (dashed vertical line) and maximized DPR (solid vertical line) against the lameness prevalence for 40 herds with cut-off (Q3 16%, horizontal black line)(▪herds with high levels of lameness, herds with low level of lameness)

Discussion

The present study shows that selected register based secondary data have a predictive ability to discriminate between high and low prevalences of lameness. Best combinations of Se and Sp were found for cow mortality (Se 100%, Sp 67%) and BMSCC (Se 70%, Sp 67%). For the two other indicators, Sp was high but at the expense of a low Se. The data-driven cut-offs with maximized DPR were lower than the predefined cut-offs. This shows that optimal cut-offs are dependent on the sample population, hence, extrapolation to the general population should only be made with great care. Lameness prevalences found in the present study corresponded well to previous Danish studies reporting herd level prevalences of severely lame cows ranging from 5–30% in cross-sectional studies [35,36,37] depending on the lameness definition. Animal welfare expert elicitation on the impact of lameness on animal welfare resulted in herd level prevalences of 9.3% [38] and 15% [20] as thresholds for unacceptable animal welfare, comparable to the chosen cut-off in the present study (16%).

Despite the general need for a multi-factorial approach in terms of assessing animal welfare, the annual Danish risk-based welfare surveillance scheme only aims at targeting livestock herds within the worst 5% range for a very limited set of indicators. Although it might be speculated that targeting a wider range of indicators covering more aspects of a dairy cow’s life like health, productivity and management would increase the sensitivity of the risk-based identification, the findings of the present study of mortality being the most potent indicator are in alignment with the current official identification scheme. Our findings emphasize the challenge of choosing the right thresholds, as the optimal cut-off for cow mortality was markedly lower than even the national means. Since the combinations of the remaining three indicators only yielded a significant difference from the random ROC curve when put in combination with cow mortality, none of these indicators proved to be better predictors than random chance; leaving mortality as the most potent indicator for high lameness prevalences. This association between cow mortality and lameness under Danish settings was expected, since locomotor disorders are the primary reason for 40% of all cases of euthanasia among Danish dairy cows [26], a finding also in alignment with other studies [24, 39,40,41]. The significant association between BMSCC and lameness found in the present study is in line with Peeler et al. [42] but in contrast to other studies looking at associations between BMSCC and lameness [43, 44] or mastitis and sole ulcers [45]. Adding the indicators BMSCC, lean cows at slaughter and SD in age at first calving in a stepwise manner to the mortality model did not improve the AUC and model quality (i.e. AIC). Nonetheless, the prediction model containing all four indicators might prove useful in other study samples. The present study used a small sample size of 40 herds yielding large CIs for the estimates and making ROC curves jagged, but still indicators showed acceptable predictive performance. However, the wide CIs imply a larger error margin, which could be decreased by increasing the sample size.

The generally lower DPR values for predefined cut-offs illustrate the pit-falls of using means or norms. Furthermore, it became obvious how essential it is to establish whether the chosen indicator should be used as an indicator and hence be used with optimal cut-offs rather than being investigated as a welfare problem per se with predefined thresholds. When using register-based indicators, it should be decided whether the variable is used solely as an indicator or whether it is assessed as a problematic condition itself, which often might be the case in the official risk-based control scheme. In case of cow mortality, this would mean that the herd specific cow mortality could be used to predict high lameness in herds based on optimized cut-offs. On the other hand, if predefined cut-offs are assigned to the herd specific mortality, it could be assessed as a problematic condition itself and not as an indicator. Therefore, the question, whether the given indicator is a risk factor for the outcome of interest or just another problem in itself, should be answered first.

Traditionally, risk-based surveillance or targeted surveillance focus on risk factors for given diseases leading to a more focused sampling of “high-risk populations” [46]. For this purpose, the quantitative measure needs to be converted into qualitative measures, a process leading to a general loss of information and to an unwanted loss in test sensitivity. Nonetheless, this conversion is essential in order to develop the first step in the risk-based surveillance scheme i.e. the identification of hazards and to stratify the population into subgroups. On the other hand, as the risk-based surveillance schemes act like initial screening tests, a high Se is needed [47], at least from the risk manager’s point of view. The subjects (herds/farm managers) being investigated would benefit from a highly specific model–avoiding false incrimination of herds with truly low lameness prevalence, although a subsequent control visit would reveal the mistake.

In order to identify herds at risk of having a given state, i.e. acceptable/unacceptable welfare or high/low lameness prevalence on a given day, certain cut-offs are established, which is rather challenging by the means of secondary incidence data. In order to mimic the official risk-based selection of herds, incidence data were restricted to cover only one year to obtain annual means of indicators for 2004. This caused differences in time periods before and after herd visits between herds, which may introduce biases e.g. due to seasonal effects. A systematic collection of register data with a fixed period before and after herd visits may have improved the predictive ability of our register data indicators. Otten et al. [31] investigated three different time periods showing differences in indicator combinations in models for each time period. If the accuracy of the current surveillance system should be improved, optimal cut-offs should be used for the risk-based sampling. However, in order to enhance the accuracy of the indicators, the general prevalence of impaired welfare should be investigated in a large-scale cross-sectional study. Furthermore, optimized cut-offs could also result in a higher number of farms initially being identified as ‘problem farms’ with an associated risk of challenging the implementation of such a surveillance due to operational constraints such as limited personnel and time.

Register-based indicators have been investigated in other surveillance settings, e.g. evaluated as naïve Bayesian classifiers to give updated probabilities of a given outcome of interest, e.g. of emerging diseases in animal populations [1, 48, 49]. However, before considering distinct models based on no-gold standard methods like latent class analysis or Bayesian methods, further investigations should be done to evaluate the effects of different time periods on the indicator performance.

Conclusions

The present study shows that the quantitative assessment of register data can be used as a screening tool for direct cross-sectional measures. Nonetheless, the present findings highlight the need for evaluating indicators and predictive models as diagnostic tests for the given case definition in order to determine their predictive performance prior to implementation in surveillance schemes. In the case of high lameness prevalence, cow mortality proved to be the strongest indicator with a high sensitivity. Adding BMSCC, lean cows at slaughter and SD in age at first calving only yielded a slight improvement in specificity. Optimized cut-offs enhanced model accuracy and should be preferred in official control schemes. Finally, the purpose of using register data needs to be clear, as outcomes will vary whether these data are used as indicators or as cut-offs for a given problem. Further investigations evaluating cut-offs for register-based indicators within different strata of the target population are needed, as there may be large variations in cut-offs between e.g. organic and conventional herds or large, intensive loose-housed dairy systems compared to small, extensive tie-stall herds.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. 1.

    Elbers AR, Bouma A, Stegeman JA. Quantitative assessment of clinical signs for the detection of classical swine fever outbreaks during an epidemic. Vet Microbiol. 2002;84:323–32.

  2. 2.

    Brouwer, H, Bartels CJM, van Wuijckhuise L, van Schaik G. Syndromic surveillance on blood samples of aborting cows for early detection of (emerging) infectious diseases using different statistical methods. In: Proceedings of the society for veterinary epidemiology and preventive medicine, March 28–30; 2012, Glasgow, United Kingdom, p. 233–44.

  3. 3.

    Perrin JP, Ducrot C, Vinard JL, Morignat E, Calavas D, Hendrikx P. Assessment of the utility of routinely collected cattle census and disposal data for syndromic surveillance. Prev Vet Med. 2012;105:244–52.

  4. 4.

    Rodríguez-Prieto V, et al. Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations. Epidemiol Infect. 2014;143(Suppl 10):1–25.

  5. 5.

    Dórea FC, McEwen BJ, McNab WB, Sanchez J, Revie CW. Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of Alerts. PLoS ONE. 2013;8:e82183. https://doi.org/10.1371/journal.pone.0082183.

  6. 6.

    Marceau A, et al. Can routinely recorded reproductive events be used as indicators of disease emergence in dairy cattle? An evaluation of 5 indicators during the emergence of bluetongue virus in France in 2007 and 2008. J Dairy Sci. 2014;97(Suppl 10):6135–50.

  7. 7.

    Vial F, Thommen S, Held N. A simulation study on the statistical monitoring of condemnation rates from slaughterhouses for syndromic surveillance: an evaluation based on Swiss data. Epidemiol Infect. 2015;143(Suppl 16):3423–33.

  8. 8.

    Antunes ACL, Dórea F, Halasa T, Toft N. Monitoring endemic livestock diseases using laboratory diagnostic data: a simulation study to evaluate the performance of univariate process monitoring control algorithms. Prev Vet Med. 2016;127:15–20.

  9. 9.

    Faverjon C, Vial F, Andersson MG, Lecollinet S, Leblond A. Early detection of West Nile virus in France: quantitative assessment of syndromic surveillance system using nervous signs in horses. Epidemiol Infect. 2017;145:1044–57.

  10. 10.

    Smith JW, Ely LO, Chapa AM. Effect of region, herd size, and milk production on reasons cows leave the herd. J Dairy Sci. 2000;83:2980–7.

  11. 11.

    Thomsen PT, Houe H. Dairy cow mortality. A review. Vet Q. 2006;28:122–9.

  12. 12.

    Alban L. Lameness in tied danish dairy cattle: the possible influence of housing systems, management, milk yield, and prior incidents of lameness. Prev Vet Med. 1996;29:135–49.

  13. 13.

    Green LE, Hedges VJ, Schukken YH, Blowey RW, Packington AJ. The impact of clinical lameness on the milk yield of dairy cows. J Dairy Sci. 2002;85:2250–6.

  14. 14.

    Duffield TF, Kelton DF, Leslie KE, Lissemore KD, Lumsden JH. Use of test day milk fat and milk protein to detect subclinical ketosis in dairy cattle in Ontario. Can Vet J. 1997;38:713–8.

  15. 15.

    Heuer C, Schukken YH, Dobbelbar P. Postpartum body condition score and results from the first test day milk as predictors of disease, fertility, yield, and culling in commercial dairy herds. J Dairy Sci. 1999;82:295–304.

  16. 16.

    de Vries M, Bokkers EA, Dijkstra T, van Schaik G, de Boer IJ. Associations between variables of routine herd data and dairy cattle welfare indicators. J Dairy Sci. 2011;94:3213–28.

  17. 17.

    Otten ND, Nielsen LR, Thomsen PT, Houe H. Register-based predictors of violations of animal welfare legislation in dairy herds. Animal. 2014;8:1963–70.

  18. 18.

    Whay HR, Main DCJ, Green E and Webster AJF. Animal-based measures for the assessment of welfare state of dairy cattle, pigs and laying hens: consensus of expert opinion. Anim Welf. 2003;12:205–17. http://www.ingentaconnect.com/content/ufaw/aw/2003/00000012/00000002/art00005 (Accessed on 18 Aug 2019).

  19. 19.

    Lievaart JJ, Noordhuizen JPTM. Ranking experts’ preferences regarding measures and methods of assessment of welfare in dairy herds using adaptive conjoint Analysis. J Dairy Sci. 2011;94:3420–7.

  20. 20.

    Sandøe P, Forkman B, Hakansson F, Andreasen SN, Nøhr R, Denwood M, Lund TB. Should the contribution of one additional lame cow depend on how many other cows on the farm are lame? Animals. 2017;7:96.

  21. 21.

    Whay HR, Waterman AE, Webster AJF. Associations between locomotion, claw lesions and nociceptive threshold in dairy heifers during the peri-partum Period. Vet J. 1997;154:155–61.

  22. 22.

    Anonymous. Report on the Welfare of Dairy Cattle. Part III: The Welfare Issues. Farm Animal Welfare Council, London, UK, 1997.

  23. 23.

    Oltenacu PA, Broom DM. The impact of genetic selection for increased milk yield on the welfare of dairy cows. Animal Welf. 2010;19:39–49.

  24. 24.

    Esslemont RJ, Kossaibati M. Culling in 50 dairy herds in England. Vet Rec. 1997;140:36–9.

  25. 25.

    Sprecher DJ, Hostetler DE, Kaneene JB. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology. 1997;47:1179–87.

  26. 26.

    Thomsen PT, Kjeldsen AM, Sorensen JT, Houe H. Mortality (including euthanasia) among Danish dairy cows (1990–2001). Prev Vet Med. 2004;62:19–33.

  27. 27.

    Ettema JF, Østergaard S. Economic decision making on prevention and control of clinical lameness in danish dairy herds. Livest Sci. 2006;102(Suppl 1–2):92–106.

  28. 28.

    Thomsen PT, Østergård S, Sørensen JT, Houe H. Loser cows in Danish dairy herds: definition, prevalence and consequences. Prev Vet Med. 2007;79:116–35.

  29. 29.

    Sandgren CH, Lindberg A, Keeling LJ. Using a national dairy database to identify herds with poor welfare. Animal Welf. 2009;18:523–32.

  30. 30.

    Nyman AK, Lindberg A, Sandgren CH. Can pre-collected register data be used to identify dairy herds with good cattle welfare? Acta Vet Scand. 2011;53(S1):S8.

  31. 31.

    Otten ND, Rousing T, Houe H, Thomsen PT, Sørensen JT. Comparison of animal welfare indices in dairy herds based on different sources of data. Animal Welf. 2016;25:207–15.

  32. 32.

    R Development Core Team . R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0;2008. http://www.R-project.org.

  33. 33.

    Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (Roc) curve. Radiology. 1982;143:29–36.

  34. 34.

    Xavier R, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.

  35. 35.

    Capion N, Thamsborg SM, Enevoldsen C. Prevalence of foot lesions in danish holstein cows. Vet Rec. 2008;163:80–5.

  36. 36.

    Klaas IC, Rousing T, Fossing C, Hindhede J, Sørensen JT. Is lameness a welfare problem in dairy farms with automatic milking systems? Anim Welf. 2003;12:599–603.

  37. 37.

    Thomsen PT, Munksgaard L, Sørensen JT. Locomotion scores and lying behaviour are indicators of hoof lesions in dairy cows. Vet J. 2012;193:644–7.

  38. 38.

    De Vries M, Bokkers EAM, van Schaik G, Botreau R, Engel B, Dijkstra T, de Boer IJM. Evaluating results of the welfare quality multi-criteria evaluation model for classification of dairy cattle welfare at the herd level. J Dairy Sci. 2013;96:6264–73.

  39. 39.

    Rajala-Schultz PJ, Gröhn YT. Culling of dairy cows Part I Effects of diseases on culling in finnish ayrshire cows. Prev Vet Med. 1999;41:195–208.

  40. 40.

    Booth CJ, Warnick LD, Gröhn YT, Maizon DO, Guard CL, Janssen D. Effect of lameness on culling in dairy cows. J Dairy Sci. 2004;87:4115–22.

  41. 41.

    Bicalho RC, Vokey F, Erb HN, Guard CL. Visual locomotion scoring in the first seventy days in milk: impact on pregnancy and survival. J Dairy Sci. 2007;90:4586–91.

  42. 42.

    Peeler EJ, Otte MJ, Esslemont RJ. Interrelationships of periparturient diseases in dairy-cows. Vet Rec. 1994;134(Suppl 6):129–32.

  43. 43.

    Mülleder CJ, Troxler J, Laaha G, Waiblinger S. Can environmental variables replace some animal-based parameters in welfare assessment of dairy cows? Animal Welf. 2007;16:153–6.

  44. 44.

    Archer SC, Green MJ, Madouasse A, Huxley JN. Association between somatic cell count and serial locomotion score assessments in UK dairy cows. J Dairy Sci. 2011;94:4383–8.

  45. 45.

    Hultgren JT, Manske T, Bergsten C. Associations of sole ulcer at claw trimming with reproductive performance, udder health, milk yield, and culling in Swedish dairy cattle. Prev Vet Med. 2004;62:233–51.

  46. 46.

    Salman MD, Stark KDC, Zepeda C. Quality assurance applied to animal disease surveillance systems. Rev Sci Tech OIE. 2003;22:689–96.

  47. 47.

    Stärk KDC, Regula G, Hernandez J, Knopf L, Fuchs K, Morris R, Davies P. Concepts for risk-based surveillance in the field of veterinary medicine and veterinary public health: review of current approaches. BMC Health Serv Res. 2006;6:20.

  48. 48.

    Shephard RW, Toribio JA, Thomsen AR, Thomson P, Baldock FC. Incorporating the Bovine Syndromic Surveillance System (BOSSS) within an animal health surveillance network. In: Proceedings of the 11th symposium of the international society for veterinary epidemiology and economics, Cairns, Aug. 2006. http://www.sciquest.org.nz/elibrary/edition/5053.

  49. 49.

    Geenen PL, van der Gaag LC, Loeffen WLA, Elbers ARW. Constructing naïve Bayesian classifiers for veterinary medicine: a case study in the clinical diagnosis of classical swine fever. Res Vet Sci. 2011;91:64–70.

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Acknowledgements

Not applicable.

Prior publication

Register data included in this article have previously been published in the Proceedings of the SVEPM Conference, Nantes, France, July 24– 26 2010.

Funding

This study was (co-) funded by the University of Copenhagen and Aarhus University.

Author information

The study was planned and designed by all authors. PTT provided lameness data from the 40 herds while NDO retrieved and prepared the register data. Data analysis was conducted by NDO and NT. HH contributed throughout the entire process from design to preparation of the manuscript. All authors have read and approved the final version of the manuscript.

Correspondence to Nina Dam Otten.

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Otten, N.D., Toft, N., Thomsen, P.T. et al. Evaluation of the performance of register data as indicators for dairy herds with high lameness prevalence. Acta Vet Scand 61, 49 (2019) doi:10.1186/s13028-019-0484-y

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Keywords

  • Dairy cattle
  • Indicators
  • Lameness
  • Register data
  • ROC