Skip to main content
main-content

01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Public Health 1/2015

Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models

Zeitschrift:
BMC Public Health > Ausgabe 1/2015
Autoren:
Supriya Kumar, Kaitlin Piper, David D. Galloway, James L. Hadler, John J. Grefenstette
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12889-015-2284-2) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

SK conceptualized the study. SK and JG designed the experiments. JG and DDG developed the model. SK carried out experiments. SK and KNP analyzed data and wrote the paper. JJG and JLH edited the paper. All authors read and approved the final manuscript.

Authors’ information

Not applicable

Availability of data and materials

Not applicable

Abstract

Background

In New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates.

Methods

Using agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities.

Results

Simulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts—a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality.

Discussion

To our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities.

Conclusion

Differential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities.
Zusatzmaterial
Additional file 1: Appendix for “Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models”. (PDF 642 kb)
12889_2015_2284_MOESM1_ESM.pdf
Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 1/2015

BMC Public Health 1/2015 Zur Ausgabe