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

Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates
Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates
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.
1-10
Zu Erbach-Schoenberg, Elisabeth
9a1f59b2-c661-42c9-ad94-96772c292add
Alegana, Victor A.
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Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Lourenco, Christoper
2bc7b120-e9ef-4db0-919c-299fe60d51a3
Ruktanonchai, Nick W.
fe68cb8d-3760-4955-99fa-47d43f86580a
Graupe, Bonita
b4dac90a-81b5-48a2-94be-695922d0e5af
Bird, Tomas J.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Zu Erbach-Schoenberg, Elisabeth
9a1f59b2-c661-42c9-ad94-96772c292add
Alegana, Victor A.
6fdaa47e-c08c-48bc-b881-1dc7b89085e4
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Lourenco, Christoper
2bc7b120-e9ef-4db0-919c-299fe60d51a3
Ruktanonchai, Nick W.
fe68cb8d-3760-4955-99fa-47d43f86580a
Graupe, Bonita
b4dac90a-81b5-48a2-94be-695922d0e5af
Bird, Tomas J.
b491394a-2b91-42d5-8262-d1c0e9ff17cd
Pezzulo, Carla
876a5393-ffbd-479a-9edf-f72a59ca2cb5
Wesolowski, Amy
343b0df8-5a2f-46e2-9f1c-001d4adf7fb1
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Zu Erbach-Schoenberg, Elisabeth, Alegana, Victor A., Sorichetta, Alessandro, Linard, Catherine, Lourenco, Christoper, Ruktanonchai, Nick W., Graupe, Bonita, Bird, Tomas J., Pezzulo, Carla, Wesolowski, Amy and Tatem, Andrew J. (2016) Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates. Population Health Metrics, 14 (35), 1-10. (doi:10.1186/s12963-016-0106-0).

Record type: Article

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.

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More information

Accepted/In Press date: 5 October 2016
e-pub ahead of print date: 12 October 2016
Organisations: Global Env Change & Earth Observation, WorldPop, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 401541
URI: https://eprints.soton.ac.uk/id/eprint/401541
PURE UUID: ba9d4104-4a0e-4190-8d76-4f445136dd0b
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 13 Oct 2016 15:10
Last modified: 15 Aug 2019 00:36

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Contributors

Author: Elisabeth Zu Erbach-Schoenberg
Author: Victor A. Alegana
Author: Catherine Linard
Author: Nick W. Ruktanonchai
Author: Bonita Graupe
Author: Tomas J. Bird
Author: Carla Pezzulo
Author: Amy Wesolowski
Author: Andrew J. Tatem ORCID iD

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