Assessment of subclinical mastitis diagnostic accuracy by differential cell count in individual cow milk

Figures & data

Table 1. Characteristics of the herds considered.

Herd	A	B	C	D
Average cows in milk	165	86	102	87
Breed	Holstein, Brown Swiss	Holstein	Holstein	Holstein, Italian Simmental
Milking parlour	Rotary	AMS	Parallel	Herringbone
Feeding	Total mix ration	Free forage, automaticfeeding concentration	Total mix ration	Total mix ration
Milk destination	Cheese	Organic	Fluid milk	Cheese
Average milk production, kg	36.2	32.6	32.6	29.2
Average somatic cell count	178,000	206,000	373,000	239,000

AMS: Automatic milking system.

Table 2. Definitions and abbreviations of the epidemiological measures reported in the text, tables and figures.

Parameter	Abbreviation	Definition
Sensitivity	SENS	Proportion of positive cases that are detected by the test, calculated as true positives/(true positives + false negatives)
Specificity	SPEC	Proportion of negative cases that are detected by the test, calculated as true negatives/(true negatives + false positives)
Lower bound (95%)	LB95	Lower limit of the calculated proportion with 95% confidence
Upper bound (95%)	UB95	Upper limit of the calculated proportion with 95% confidence
Positive predictive value	PPV	Proportion of truly positive cases among the positive cases detected by the test, calculated as true positives/(true positives + false positives)
Negative predictive values	NPV	Proportion of truly negative cases among the negative cases detected by the test, calculated as true negatives/(true negatives + false negative)
Positive likelihood ratio	LR+	This ratio indicates to which point an individual has more chances to be positive in reality when the test is positive, calculated as sensitivity/(1-specificity)
Negative likelihood ratio	LR-	This ratio indicates to which point an individual has more chances to be positive in reality when the test is negative, calculated as (1-sensitivity)/specificity
Accuracy	ACC	the accuracy of a measurement system is the degree of closeness of measurements to true value
Area under the curve	AUC	It is the probability that a positive event is classified as positive by the test given all possible values of the test. For an ideal model AUC =1

Figure 1. ROC curve calculated on all the milk test record (MTR) considered (4386) without classification. Abbreviations refer to parameters described in Table 2 and are related to the identification of subclinical mastitis by differential cell counts (DSCC)

Figure 2. ROC curves calculated classifying the samples based on parity. Abbreviations refer to parameters described in Table 2 and are related to the identification of subclinical mastitis by differential cell counts (DSCC)

Figure 3. ROC curves calculated classifying the samples based on days in milk. Abbreviations refer to parameters described in Table 2 and are related to the identification of subclinical mastitis by differential cell counts (DSCC)

Figure 4. ROC curves calculated classifying the samples based on parity (PAR) and days in milk (DIM) (P100: PAR =1 DIM ≤100; P200: PAR >1 DIM >100; PL100: PAR >1 DIM ≤100; PL200: PAR >1 DIM >200). Abbreviations refer to parameters described in Table 2 and are related to the identification of subclinical mastitis by DSCC. PAR: parity; DIM: days in milk; DSSC: differential cell count.

Table 3. Summary of the parameters calculated to evaluate test performances based on the different methods of sample classification.

Classification, N classes	SENS	SPEC	PPV	NPV	SENS + SPEC	ACC	AUC
Parity (3)	0.765	0.840	0.579	0.924	1.605	0.824	0.868
Days in milk (3)	0.801	0.835	0.573	0.931	1.636	0.823	0.883
Parity and days in milk (4)	0.819	0.810	0.514	0.943	1.493	0.810	0.878
Overall	0.763	0.831	0.567	0.924	1.594	0.816	0.869

Abbreviations refer to parameters described in Table 2 and are related to the identification of subclinical mastitis by DSCC. Bold characters show the highest value observed for each parameter. DSSC: differential cell counts.