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Dynamic Denominators: The Impact of Seasonally Varying Population Numbers on Disease Incidence Estimates

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Dynamic Denominators: The Impact of Seasonally Varying Population Numbers on Disease Incidence Estimates

Elisabeth Zu Erbach-Schoenberg et al. Popul Health Metr.

Abstract

Background: Reliable 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.

Methods: We 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.

Results: We 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.

Conclusion: The 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.

Keywords: Disease incidence; Health metrics; Malaria; Mobile phones; Seasonality; Surveillance.

Figures

Fig. 1
Fig. 1
Population size, malaria incidence, and mobile phone ownership in Namibia: a Population numbers per health district according to 2011 census, b Annual parasite incidence 2011 using census population numbers as denominator, c mobile phone ownership according to DHS 2013
Fig. 2
Fig. 2
Health facility locations and mobile phone tower density: Health facility locations for facilities with completed case reports. Colour of health districts according to tower density as towers per 1000 km2
Fig. 3
Fig. 3
CDR data processing method illustration: a Extracting unique users per tower from raw CDR data. b Redistribution of user counts from tower level to health district level based on areas of intersection
Fig. 4
Fig. 4
Seasonal changes in population numbers: Difference in predicted population number between November and December 2011 for each health district. Insets show predicted population number for selected health districts over the whole study period
Fig. 5
Fig. 5
Difference in incidence estimates using dynamic and static denominators: Difference between dynamic and static incidence as percent of dynamic incidence estimate. Colour of lines according to malaria risk zone classification of the corresponding health district as shown in inset map
Fig. 6
Fig. 6
Difference between dynamic and static incidence for January 2012: Difference between dynamic and static incidence as percent of dynamic incidence estimate for each health district for January 2012. Red indicating overestimation of incidence using the static denominator and blue corresponding to potential underestimation. Insets show the dynamic incidence for selected health districts over the whole study period

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