Population movement can sustain STI prevalence in remote Australian indigenous communities

Abstract

BackgroundFor almost two decades, chlamydia and gonorrhoea diagnosis rates in remote Indigenous communities have been up to 30 times higher than for non-Indigenous Australians. The high levels of population movement known to occur between remote communities may contribute to these high rates.MethodsWe developed an individual-based computer simulation model to study the relationship between population movement and the persistence of gonorrhoea and chlamydia transmission within hypothetical remote communities.ResultsResults from our model suggest that short-term population movement can facilitate gonorrhoea and chlamydia persistence in small populations. By fixing the number of short-term travellers in accordance with census data, we found that these STIs can persist if at least 20% of individuals in the population seek additional partners while away from home and if the time away from home is less than 21 days. Periodic variations in travel patterns can contribute to increased sustainable levels of infection. Expanding existing STI testing and treatment programs to cater for short-term travellers is shown to be ineffective due to their short duration of stay. Testing and treatment strategies tailored to movement patterns, such as encouraging travellers to seek testing and treatment upon return from travel, will likely be more effective.ConclusionHigh population mobility is likely to contribute to the high levels of STIs observed in remote Indigenous communities of Australia. More detailed data on mobility patterns and sexual behaviour of travellers will be invaluable for designing and assessing STI control programs in highly mobile communities.

Exploring the Benefits of Molecular Testing for Gonorrhoea Antibiotic Resistance Surveillance in Remote Settings

“…An individual-based mathematical model was developed to describe the transmission of gonorrhoea in a remote Indigenous population in Australia. The model is an extension of a previously published model that we used to evaluate the importance of population mobility for gonorrhoea transmission in remote Indigenous communities of Australia [ 13 ]. While the model is parameterised using demographic, sexual behaviour and mobility data for this setting, our approach can be applied to other settings.…”

Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities

“…An individual-based model, repurposed from a framework developed to examine dynamics of sexually transmitted infections in remote Australia, is used to explicitly represent each community member [10].…”

001.6 Exploring the benefits of molecular testing for gonorrhoea antibiotic resistance surveillance in remote settings

“…The modelled population represents sexually active individuals aged 15-35 years residing in one larger centre (comprising 4000 individuals) and four small communities (comprising 250 individuals each) that are "satellites" of the larger centre from a population mobility perspective [13]. Gender-and age-specific mortality rates were adjusted to maintain the gender-age distribution as described in census data [14].The model tracks age, gender, location, gonorrhoea infection status and sexual behaviour on a daily basis, while population mobility is captured based on census data using modelling techniques as previously described [13].…”

“…Gender-and age-specific mortality rates were adjusted to maintain the gender-age distribution as described in census data [14].The model tracks age, gender, location, gonorrhoea infection status and sexual behaviour on a daily basis, while population mobility is captured based on census data using modelling techniques as previously described [13]. Table 1 lists the demographic and behavioural parameters used in the model.…”

“…Furthermore, there is no substantive evidence suggesting that the natural history of treatment-resistant strains is different from that of treatment-sensitive strains. We Probability of seeking additional partners while away from home 5% of population [13] Proportion of population that have at least one regular partners in past 6 months 54.8% (a) [32], adjusted to exclude individuals with no partnerships…”