Estimating the population attributable fraction for schizophrenia when Toxoplasma gondii is assumed absent in human populations

•There is evidence that Toxoplasma gondii may be a component cause of schizophrenia.•We describe a new method for estimating the population attributable fraction (PAF).•The PAF for schizophrenia in those exposed to T. gondii is tentatively 21.4%.•The PAF could be up to 30.6% in some countries although many uncertainties exist.

There is increasing evidence that infection with Toxoplasma gondii, a common parasite of people, cats and rodents, is associated with an increased risk of a diagnosis of schizophrenia. Although the claim that infection with T. gondii is one of the component causes of a diagnosis of schizophrenia remains contentious it is worth asking how important a causal association might be if only to inform our attitude to further work on the subject. The appropriate measure of importance is the population attributable fraction (PAF). The PAF is the proportion of diagnoses of schizophrenia that would not occur in a population if T. gondii infections were not present. The assumptions that underlie the derivation of the standard formula for measuring the PAF are violated in the specific instance of T. gondii and schizophrenia and so the conventional estimation method cannot be used. Instead, the PAF was estimated using a deterministic model of Toxoplasma gondii infection and schizophrenia occurrence in a hypothetical cohort of people at risk of both conditions. The incidence of infection with T. gondii in the cohort was assumed to be constant. Under these circumstances, the life-time mean population attributable fraction was estimated to be 21.4%, but it could not be ruled out that it could be as high as 30.6% or as low as 13.7% given the 95% confidence interval pertaining to the point estimate of the OR that was central to the calculation. These estimates (even the lowest) are higher than those obtained using the standard method for the same system and underscore the importance of understanding the limitations of conventional epidemiological formulae.