Using crowdsourcing to estimate populations in communities: Providing a key measurement for coverage assessment of community programmes.

INTRODUCTION

Crowdsourcing pools together dispersed information that is considered public knowledge in an area, to form realistic estimates about the area, or to identify new ideas. The technique can be extremely helpful to develop estimates of public health indicators such as catchment area populations or healthcare providers; however, such uses must be scientifically validated.

METHODS

We divided the community into 1040 discrete segments of similar lengths of streets (called spots) and then randomly selected 605 of these spots for crowdsourcing. Local respondents were asked to estimate the maximum and the minimum population residing in those spots. Five informants were interviewed per spot. Median values for the maximum and minimum were averaged to arrive at an estimate for the spot's population. Estimates for all spots were added together to arrive at the population of the community. One hundred spots from the 597 crowdsourced spots were revisited to conduct a household census as a "gold standard".

RESULTS

Spots where both crowdsourcing and census estimates were computed had a crowdsource population estimate of 19,255 versus a census estimate of 18,119 - a variation of 5.9% (p: <0.001). However, within spot variation was a mean of 25%.

CONCLUSIONS

Crowdsourcing communities for public knowledge information can yield more accurate information about public health indicators such as populations. In turn these estimates can help to better understand public health programme coverage. Other applications to consider may be missed children for immunization or schooling, deaths or births in communities or to identify total formal or informal healthcare providers in a community.