Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates

Abstract

BackgroundReliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.MethodsWe show how mobile phone data can be used to measure seasonal changes in health district population numbers, which are used as denominators for calculating district-level disease incidence. Using the example of malaria case reporting in Namibia we use 3.5 years of phone data to investigate the spatial and temporal effects of fluctuations in denominators caused by seasonal mobility on malaria incidence estimates.ResultsWe show that even in a sparsely populated country with large distances between population centers, such as Namibia, populations are highly dynamic throughout the year. We highlight how seasonal mobility affects malaria incidence estimates, leading to differences of up to 30 % compared to estimates created using static population maps. These differences exhibit clear spatial patterns, with likely overestimation of incidence in the high-prevalence zones in the north of Namibia and underestimation in lower-risk areas when compared to using static populations.ConclusionThe results here highlight how health metrics that rely on static estimates of denominators from censuses may differ substantially once mobility and seasonal variations are taken into account. With respect to the setting of malaria in Namibia, the results indicate that Namibia may actually be closer to malaria elimination than previously thought. More broadly, the results highlight how dynamic populations are. In addition to affecting incidence estimates, these changes in population density will also have an impact on allocation of medical resources. Awareness of seasonal movements has the potential to improve the impact of interventions, such as vaccination campaigns or distributions of commodities like bed nets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-016-0106-0) contains supplementary material, which is available to authorized users.

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…In many low-resource settings, for instance, the users are commonly disproportionately male, educated and from larger households, compared with the general population. 20,85,86 Moreover, the behaviours of using mobile phones and social media as well as the possibility that individuals own multiple SIM cards or mobiles affect the ability to produce accurate and representative estimates of population mobility. 20,23,25 Though these potential biases are decreasing as mobile phone ownership rises, 20 a prerequisite for these studies is still to understand the demographic features of mobile phone owners or users of social media and mHealth apps.…”

“…35,53 Mobile phone data are particularly promising for analysing travel-related phenomena on a scale previously impossible, providing a 'big data' approach to understanding human mobility and its changes. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Two types of mobile-based positioning data that have so far been increasingly explored in travel-related studies are call detail records (CDRs) and mobile location history.…”

“…CDRs are routinely collected by mobile phone operators for billing purposes. 20,31 Each CDR contains an entry for each call or text made or received by any user with the subscriber identification module (SIM) card, together with the date and time of each communication and the tower that the communication was routed through within mobile phone networks. 23 ,24 Every time an individual makes a call or sends a text via a short messaging service, it normally will be routed through the closest tower in the network.…”

“…58 Furthermore, these passive positioning data derived from CDRs can also be used to measure seasonal changes in subnational population numbers and produce density maps of human distribution changes over multiple timescales, providing more precise denominators for health metrics than static measures from censuses. 20 However, CDRs cannot measure spatial movements finer than tower-level spatial resolution, and estimates are limited to domestic movements, as it is more difficult to obtain CDRs from operators in different countries to get estimates of international traveller flows. Nevertheless, mobile phone location history data are promising for measuring cross-border movements, as outlined below.…”

“…18 However, many recent studies have highlighted how our understanding of human mobility across contexts can be significantly improved through quantitative analyses of positioning data from the huge population of mobile phone users. 19,20 In 2017, there were already over 5 billion unique mobile subscribers globally, with a penetration rate of 66% of the global population, and the total number of mobile cellular subscriptions exceeds the world population at 7.79 billion. 21,22 Moreover, mobile phone penetration is constantly rising and is predicted to nearly reach 6 billion users by 2025 with 5 billion connecting to internet.…”