Four methods were developed and evaluated as potential candidates for an algorithm that is suitable for standardizing crude PfPR data. Of the four methods, the selected algorithm based on a modified version of Pull & Grab's equations [28] was clearly superior to all others. The algorithm explained 72% of the variance in PfPR estimates on a set of independent data, and it was superior to the predictions obtained through linear regression of that data. The mathematical model on which the algorithm was based was biologically motivated and fitted to a set of highly stratified PfPR data that spanned the range of *P. falciparum* transmission intensity. The analysis suggests that the relationship between age and PfPR is fairly consistent across the range of *P. falciparum* transmission intensity and that it is strongly determined by the underlying biology. The algorithm works better than regression because it captures more information about the general patterns in PfPR-age relationships than the 121 averaged PfPR pairs.

While the algorithm permits age-standardization of PfPR for the heterogeneous way of reporting it, this would be unnecessary if minimum standards were followed for reporting PfPR data. Because PfPR varies by age, published estimates of PfPR should always be stratified by age; the 21 training sets listed here have set a reasonable bar, with some important caveats. This analysis suggests that reporting the average PfPR for the 2–10 year old age classes can often be done without loss of information, but the data should first be checked for the typical pattern. For example, PfPR did not reach the plateau until age 5 in some studies. The PfPR begins to decline in older children sometime after age 10 because of blood stage immunity, except possibly at very low PfPR, so PfPR data should be stratified by age in older children and adults. A sensible rule would be to bin by year at least through age 15, and then to bin by at most 5-year age groups after age 20.

For children younger than 2, the PfPR should be reported at a grain that is fine enough to describe changes in PfPR, subject to the social, logistic and ethical concerns of recruiting sufficient infants and young children to the survey. PfPR increases with age in very young children as they acquire their first infections, develop clinical malaria, and then either clear the infections with antimalarial drugs and await reinfection, or recover from symptoms and maintain an asymptomatic infection. Reinfection and clinical malaria continue throughout life, but the frequency and severity of clinical malaria decline as clinical immunity develops [33]. As a consequence, so does the rate at which parasites are cleared by antimalarial drugs. Thus, the expected time to clinical malaria gets longer as children grow older, and this can affect the use of antimalarial drugs. The rate at which children are bitten also increases as their body size increases, and they thus absorb a greater proportion of the bites in the household [34]. Thus, the expectation is that PfPR increases rapidly during these years.

This analysis suggests that after the first few years of life, PfPR settles to a plateau where it remains fairly constant until the onset of adolescence. The plateau reflects the original steady state that was the motivation for using PfPR to index transmission intensity, a population dynamic equilibrium at which parasite clearance is balanced by new infections, and simultaneously not biased by detection issues associated with immunity and microscopy [26, 27]. The rise in PfPR with age has been suggested as an alternative index of transmission intensity [35], but this analysis found substantial variability in the slope that was uncorrelated with the PfPR in 2–10 year olds (Figure 3). The discrepancy between the two raises the question of which measure provides a better index of transmission intensity.

For practical and epidemiological reasons, the PfPR in older children provides a good index of transmission intensity, and the age limits from the classical categories are appropriate for standardizing PfPR. There are three justifications for standardizing reported PfPR to 2–10 year olds for use as an index of transmission intensity. First, because PfPR remains relatively constant between the ages of about two and 10, the average is therefore meaningful. Second, because the frequency of clinical malaria declines in older children, the PfPR over these ages may be least influenced by drug treatment [36]. PfPR in older children is also relatively unaffected by immunity, so it can be argued that the PfPR in older children comes closest to reflecting the steady state relationship predicted by mathematical models. It is, therefore, most likely to provide a good index of transmission intensity. Third, it is consistent with the historical approaches to defining endemicity [18].

Using the rise in PfPR in very young children to index transmission has some advantages, but it also has several disadvantages. Although young children are of great interest for malaria control, the slope of PfPR, not the average PfPR in young children, is an index of transmission intensity. From a practical point of view, it is more difficult to measure a slope than an average. The ideal measure of transmission intensity would be the infant conversion rate, characterized by the waiting time to a patent infection in uninfected children. A slightly less direct measure of the force of infection would be any exposure to malaria vs. age, a measure that would be found in an appropriately designed study of seroprevalence [37, 38]. PfPR is a distant third because it reflects a balance between exposure and clearance, including natural clearance and radical curative therapy with antimalarial drugs. The frequency of clinical malaria is highest in young children, before they develop functional immunity, so the need for antimalarial drugs is also likely to be correlated with age. The rise in PfPR reflects both exposure and clearance, so it can be quite a volatile measure. For example, the rise in PfPR in young children would dramatically underestimate transmission intensity in populations where effective antimalarial drugs had been used frequently and properly to clear infections. Because gametocytes clear slowly, it has also been argued that measuring gametocyte prevalence directly is a more appropriate measure of exposure, but considerable technical difficulties still limit its wider application [39].

The relationship between age and PR follows a fairly consistent pattern across the natural range of malaria transmission intensity that can be described by a relatively simple, biologically motivated mathematical model. The model, fitted to age-stratified data, accurately describes the rise and fall in PfPR with age, but it does not resolve any questions about the biological causes of this pattern, since it was not designed to do so. The algorithm based on this model can be used to reliably standardize crude estimates, but some important residual variance remains unexplained. This could be due to a variety of factors that were not considered here, including sampling, seasonality, heterogeneous biting, the prevalence of *P. vivax*, and variable use of effective anti-malarial drugs. These will be the focus of future research effort. Despite these caveats, the algorithm explained 72% of the variance, suggesting that age is a dominant source of heterogeneity in PfPR estimates from places with similar transmission intensity. The algorithm thus provides a useful way of standardizing PfPR to 2–10 year olds for comparing studies and ultimately for the mapping of malaria risk.