Prioritizing Zoonoses: A Proposed One Health Tool for Collaborative Decision-Making

<div><p>Emerging and re-emerging zoonotic diseases pose a threat to both humans and animals. This common threat is an opportunity for human and animal health agencies to coordinate across sectors in a more effective response to zoonotic diseases. An initial step in the collaborative process is identification of diseases or pathogens of greatest concern so that limited financial and personnel resources can be effectively focused. Unfortunately, in many countries where zoonotic diseases pose the greatest risk, surveillance information that clearly defines burden of disease is not available. We have created a semi-quantitative tool for prioritizing zoonoses in the absence of comprehensive prevalence data. Our tool requires that human and animal health agency representatives jointly identify criteria (e.g., pandemic potential, human morbidity or mortality, economic impact) that are locally appropriate for defining a disease as being of concern. The outcome of this process is a ranked disease list that both human and animal sectors can support for collaborative surveillance, laboratory capacity enhancement, or other identified activities. The tool is described in a five-step process and its utility is demonstrated for the reader.</p></div

An example of decision tree analysis (Step 5 in the OHZDP Tool) for rabies.

<p>The criteria and questions shown are examples only, provided to show the process of how each zoonotic disease is scored. Criteria and questions are developed and given weights by the stakeholder representatives during the facilitated group work in Steps 2–5. Weighted scores for each question are summed to give the total weighted score for each pathogen; total weighted scores are normalized in relation to the maximum pathogen score to give a final ranked list.</p

Example criteria and categorical questions used in Steps 2 and 3 of the OHZDP Tool to prioritize zoonotic diseases (ZD).

<p>*The handout is provided to participants to stimulate conversation and is not intended as an exhaustive list of possibilities.</p>†<p>Only one categorical question is chosen to represent each criterion.</p><p>Example criteria and categorical questions used in Steps 2 and 3 of the OHZDP Tool to prioritize zoonotic diseases (ZD).</p

Methods used for criteria selection, weighting and scoring of pathogens.

<p>*The nature of the questions used in the decision tree will determine if the process is quantitative or semi-quantitative.</p><p>Methods used for criteria selection, weighting and scoring of pathogens.</p

The five steps of the prioritization process using the One Health Zoonotic Disease Prioritization Tool.

<p>The five steps of the prioritization process using the One Health Zoonotic Disease Prioritization Tool.</p

Summary of publications on the prioritization of infectious diseases at the national or regional level^*.

<p>*Only publications that include a final ranked list of pathogens are referenced in the table.</p><p>Summary of publications on the prioritization of infectious diseases at the national or regional level^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109986#nt102" target="_blank">*</a>}.</p

Prevalence of Obesity in Children at Age 9 y, by Mother's Age at Menarche

<p>Data are proportions (± 95% CI) of children with obesity (BMI >97th percentile according to the UK 1990 growth reference [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0040132#pmed-0040132-b019" target="_blank">19</a>]) in each quintile of mother's menarche from the whole ALSPAC cohort (boys: <i>p</i>-value for trend = 0.003, <i>n</i> = 2,961; girls: <i>p</i>-value for trend = 0.006, <i>n</i> = 3,048), adjusted for age, mother's education, and also for mother's BMI.</p

Contrasting Early Postnatal Growth Patterns in Offspring of Mothers with Earlier or Later Menarche

<p>Unadjusted weight SD scores are shown in children grouped by extreme quintiles of their mother's age at menarche. Data are means ± standard error from the Children in Focus subgroup (<i>n</i> = 914).</p

Frequency of serum aflatoxin B–lysine albumin adduct concentrations for participants

<p><b>Copyright information:</b></p><p>Taken from "Case–Control Study of an Acute Aflatoxicosis Outbreak, Kenya, 2004"</p><p>Environmental Health Perspectives 2005;113(12):1779-1783.</p><p>Published online 9 Aug 2005</p><p>PMCID:PMC1314920.</p><p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.</p