Incidence, prevalence and other epidemiological measures
Measuring disease in animal populations: incidence, prevalence, mortality, risk and odds ratios, sensitivity, specificity and predictive values.
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"Lameness is a problem in this herd" is a feeling. "Twenty-five per cent of cows are lame today, and 40 new cases per 100 cows occurred last year" is a measurement that can be compared with other herds, tracked over time and used to judge whether a change in management worked. Veterinary epidemiology studies disease in populations rather than individuals: who gets sick, where, when and why. It starts with a small set of standard measures that every herd adviser and veterinary officer should be able to calculate.
Epidemiological measures and diagnostic test calculations are core topics in veterinary epidemiology, public health and herd health exams.
Counting disease: prevalence and incidence
- Prevalence: the proportion of animals that have the disease at a given time.
- Point prevalence = cases at one time ÷ population at that time.
- Period prevalence = animals with the disease at any time during a period ÷ average population.
- Incidence: how often new cases occur.
- Cumulative incidence (risk) = new cases during a period ÷ animals at risk at the start; for example, 30 of 200 calves developing diarrhoea in their first month gives a risk of 15 per cent.
- Incidence rate = new cases ÷ total animal-time at risk (such as cow-years), which handles animals entering and leaving the population.
For a stable situation, prevalence ≈ incidence rate × average duration of disease: a disease with low incidence but long duration, such as Johne's disease, can have high prevalence; a short, acute disease may have high incidence but low prevalence at any moment.
Other common measures
- Attack rate: cumulative incidence during an outbreak.
- Morbidity: the proportion of animals affected.
- Mortality rate: deaths from a disease (or all causes) ÷ population at risk.
- Case fatality: deaths ÷ animals with the disease, a measure of severity.
Measuring association
To test whether a factor, such as a feed, housing type or vaccination, is linked to disease, epidemiologists compare exposed and unexposed groups:
- Relative risk (RR) = risk in exposed ÷ risk in unexposed. RR = 1 means no association; above 1, the factor increases risk.
- Odds ratio (OR) = (a × d) ÷ (b × c) from a 2×2 table; used in case–control studies, and close to RR when the disease is rare.
- Attributable risk = risk in exposed − risk in unexposed: the extra risk due to the factor.
Study designs include cross-sectional surveys (prevalence), cohort studies (following exposed and unexposed animals forward), case–control studies (comparing past exposures of cases and controls) and randomised controlled trials.
Surveillance
Surveillance is the ongoing, systematic collection and analysis of disease data to guide action. Passive surveillance relies on farmers and vets reporting sick animals; it is cheap but misses many cases. Active surveillance goes looking: sampling animals at markets, slaughterhouses or farms, often using risk-based designs that focus on high-risk places. Syndromic surveillance tracks signs such as milk drops or abortions before a diagnosis is made. Good surveillance needs a clear case definition, representative sampling, laboratory capacity and fast feedback to those who reported.
Diagnostic tests
- Sensitivity: the proportion of truly diseased animals that test positive.
- Specificity: the proportion of truly healthy animals that test negative.
- Positive predictive value (PPV): the proportion of test-positive animals that are truly diseased.
- Negative predictive value (NPV): the proportion of test-negative animals that are truly healthy.
Sensitivity and specificity are properties of the test; predictive values depend also on prevalence. When prevalence is low, PPV falls, which is why positive screening results in disease-free areas are confirmed with a second, more specific test. Using two tests in series (both must be positive) increases specificity; in parallel (either positive) increases sensitivity.
From counts to conclusions
Diagnostic test. A test with 90 per cent sensitivity and 95 per cent specificity is used in 1,000 cows, of which 100 (10 per cent) are infected.
- True positives = 0.90 × 100 = 90; false negatives = 10.
- True negatives = 0.95 × 900 = 855; false positives = 45.
- PPV = 90 ÷ (90 + 45) = 67 per cent; NPV = 855 ÷ (855 + 10) = 98.8 per cent.
If prevalence were only 1 per cent (10 infected), there would be 9 true positives and 49.5 false positives: PPV ≈ 9 ÷ 58.5 ≈ 15 per cent.
Association. Of 100 calves fed from a dirty bucket, 30 develop diarrhoea; of 100 fed from clean buckets, 10 do.
- RR = (30 ÷ 100) ÷ (10 ÷ 100) = 3.0.
- OR = (30 × 90) ÷ (70 × 10) = 2,700 ÷ 700 ≈ 3.9.
- Attributable risk = 0.30 − 0.10 = 0.20: 20 extra cases per 100 calves due to dirty buckets.
Incidence rate. A herd records 12 new mastitis cases over 60 cow-years at risk: 12 ÷ 60 = 0.2 cases per cow-year, or 20 per 100 cow-years.
Key points
- Prevalence measures existing cases; incidence measures new cases (as risk or as a rate per animal-time).
- Prevalence ≈ incidence rate × average duration.
- Mortality, morbidity, attack rate and case fatality describe outbreaks.
- RR = risk exposed ÷ risk unexposed; OR = ad ÷ bc; attributable risk = difference in risks.
- Sensitivity = a ÷ (a + c); specificity = d ÷ (b + d); PPV = a ÷ (a + b); NPV = d ÷ (c + d).
- PPV falls when prevalence is low; confirm positives with a second, more specific test.
What the exams ask
- School-leaving and entrance papers: meaning of incidence, prevalence and mortality.
- Degree and research-fellowship entrances: calculating prevalence, incidence, RR, OR, attributable risk, sensitivity, specificity and predictive values, and study designs.
- Veterinary and public health examinations: interpreting surveillance data, herd health indicators, test strategies in eradication programmes.
Described in general terms: the kind of examination, not a past paper. Laisha never reproduces exam questions.
Questions people ask
Why is prevalence not enough to judge a disease problem?
Prevalence mixes how often disease occurs with how long it lasts. A herd with many long-standing cases may have the same prevalence as one with many short, new cases, but different causes and solutions.
Why does a good test give false positives?
No test is perfectly specific. When most animals are healthy, even a small false-positive rate among many healthy animals can outnumber the true positives.
What is the difference between risk and odds?
Risk is cases ÷ all animals at risk; odds is cases ÷ non-cases. For rare diseases they are similar; for common diseases they differ.
Sources
- Thrusfield, M. et al. Veterinary Epidemiology, 4th edition. Wiley-Blackwell, 2018.
- Dohoo, I., Martin, W. and Stryhn, H. Veterinary Epidemiologic Research, 2nd edition. VER Inc., 2009.
- Rothman, K. J., Greenland, S. and Lash, T. L. Modern Epidemiology, 3rd edition. Lippincott Williams and Wilkins, 2008.
- Noordhuizen, J. P. T. M. et al. Application of Quantitative Methods in Veterinary Epidemiology. Wageningen Pers, 1997.
- Pfeiffer, D. U. Veterinary Epidemiology: An Introduction. Wiley-Blackwell, 2010.
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