Capturing Household Structure and Mobility within and between Remote Aboriginal Communities in Northern Australia Using Longitudinal Data: A Pilot Study

Abstract

Cultural practices and development level can influence a population's household structures and mixing patterns. Within some populations, households can be organized across multiple dwellings. This likely affects the spread of infectious disease through these communities; however, current demographic data collection tools do not record these data.

Methods: Between June and October 2018, the Contact And Mobility Patterns in remote Aboriginal Australian communities (CAMP-remote) pilot study recruited Aboriginal mothers with infants in a remote northern Australian community to complete a monthly iPad-based contact survey.

Results: Thirteen mother-infant pairs (participants) completed 69 study visits between recruitment and the end of May 2019. Participants reported they and their other children slept in 28 dwellings during the study. The median dwelling occupancy, defined as people sleeping in the same dwelling on the previous night, was ten (range: 3.5-25). Participants who completed at least three responses (n = 8) slept in a median of three dwellings (range: 2-9). Each month, a median of 28% (range: 0-63%) of the participants travelled out of the community. Including these data in disease transmission models amplified estimates of infectious disease spread in the study community, compared to models parameterized using census data.

Conclusions: The lack of data on mixing patterns in populations where households can be organized across dwellings may impact the accuracy of infectious disease models for these communities and the efficacy of public health actions they inform.

Keywords: aboriginal; contact patterns; disease transmission; household model; household structure; human mobility; indigenous.

Figure A1

Summary of recruitment and response rates by participant and by month.

Figure A2

(a) Mean number of social contacts per CAMP-remote participant per day (n = 13). (b) Number of social contacts per adult, female participants (20–49 years) per day in a community in rural coastal Kenya collected by Kiti et al. (n = 74) [16]. The definition of contact for Kiti et al.’s study was anyone with whom they had physical contact [16], while the definition for the CAMP-remote study was anyone with whom they had spent an hour or more.

Figure 1

Clustering of spatial coordinates to identify distinct dwellings (n = 28). Each symbol represents a survey response. Participants with one or two responses are depicted as grey circles, all other participants (n = 8) are depicted using a unique symbol.

Figure 2

Distribution of the mean number of people per dwelling by age group. Each dot represents the mean number of individuals of a specific age across all survey responses for a given dwelling. The median of the means is represented using the thick grey line and the interquartile range of the means with the thinner grey lines.

Figure 3

Distribution of the number of people per dwelling for dwellings with five or more nights of data. Each dot represents the total number of individuals in each dwelling for a specific survey response. The median is represented using the thick grey line and the interquartile range with the thinner grey lines.

Figure 4

Median number of mean dwelling contacts between each age category per dwelling using the CAMP-remote data. Confidence intervals of 95%, estimated with a nonparametric bootstrap method, are indicated in brackets. Age ranges were young (less than 5 years), school-aged (5–14 years) and adult (15+ years).

Figure 5

Cumulative distribution of the mean number of social contacts per CAMP-remote participant (n = 13) per day. Shaded area represents 95% confidence interval.

Figure 6

(a) The prevalence of infection arising from simulations of an influenza-like SEIR model under different mixing assumptions. (b) The prevalence of infection arising from simulations of an endemic disease in an SEIS model under different mixing assumptions. Both models are parameterized to reflect the study community.