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. 2021 Aug 25;15(8):e0009700.
doi: 10.1371/journal.pntd.0009700. eCollection 2021 Aug.

Estimating underreporting of leprosy in Brazil using a Bayesian approach

Guilherme L de Oliveira  1 , Juliane F Oliveira  2 , Júlia M Pescarini  2   3 , Roberto F S Andrade  2   4 , Joilda S Nery  5 , Maria Y Ichihara  2 , Liam Smeeth  6   7 , Elizabeth B Brickley  3 , Maurício L Barreto  2   5 , Gerson O Penna  8   9 , Maria L F Penna  10 , Mauro N Sanchez  8   11
Affiliations

Estimating underreporting of leprosy in Brazil using a Bayesian approach

Guilherme L de Oliveira et al. PLoS Negl Trop Dis. .

Abstract

Background: Leprosy remains concentrated among the poorest communities in low-and middle-income countries and it is one of the primary infectious causes of disability. Although there have been increasing advances in leprosy surveillance worldwide, leprosy underreporting is still common and can hinder decision-making regarding the distribution of financial and health resources and thereby limit the effectiveness of interventions. In this study, we estimated the proportion of unreported cases of leprosy in Brazilian microregions.

Methodology/principal findings: Using data collected between 2007 to 2015 from each of the 557 Brazilian microregions, we applied a Bayesian hierarchical model that used the presence of grade 2 leprosy-related physical disabilities as a direct indicator of delayed diagnosis and a proxy for the effectiveness of local leprosy surveillance program. We also analyzed some relevant factors that influence spatial variability in the observed mean incidence rate in the Brazilian microregions, highlighting the importance of socioeconomic factors and how they affect the levels of underreporting. We corrected leprosy incidence rates for each Brazilian microregion and estimated that, on average, 33,252 (9.6%) new leprosy cases went unreported in the country between 2007 to 2015, with this proportion varying from 8.4% to 14.1% across the Brazilian States.

Conclusions/significance: The magnitude and distribution of leprosy underreporting were adequately explained by a model using Grade 2 disability as a marker for the ability of the system to detect new missing cases. The percentage of missed cases was significant, and efforts are warranted to improve leprosy case detection. Our estimates in Brazilian microregions can be used to guide effective interventions, efficient resource allocation, and target actions to mitigate transmission.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Brazilian microregions: (a) Observed leprosy incidence per 100,000 inhabitants in the period from 2007 to 2015; (b) Proportion of new leprosy cases diagnosed with Grade 2 of physical disabilities from 2007 to 2015.
Similar plots at state level are presented in S1 Fig. Raw data are shown in this figure and available at https://github.com/cidacslab/Estimating-under-reporting-of-leprosy-in-Brazil.git [31]. We produced the maps using R software, geobr package [31, 32], (MIT license https://ipeagit.github.io/geobr/).
Fig 2
Fig 2. Brazilian microregions: (a) Mean leprosy incidence rate per 100,000 inhabitants in the period from 2007 to 2015 corrected by underreporting; (b) Estimated probability of reporting a leprosy case in each Brazilian microregion.
Similar plots at state level are presented in the S1 Fig. Raw data are shown in this figure and available at https://github.com/cidacslab/Estimating-under-reporting-of-leprosy-in-Brazil.git [31]. We produced the maps using R software, geobr package [31, 32], (MIT license https://ipeagit.github.io/geobr/).
See this image and copyright information in PMC

References

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