Estimating Under Reporting of Leprosy in Brazil using a Bayesian Approach

Oliveira, Guilherme Lopes de; Oliveira, Juliane F.; Andrade, Roberto F. S.; Nery, Joilda Silva; Pescarini, Júlia Moreira; Ichihara, Maria Yury; Smeeth, Liam; Brickley, Elizabeth B.; Barreto, Maurício Lima; Penna, Gerson Oliveira; Penna, Maria Lúcia Fernandes; Sanchez, Mauro Niskier

doi:10.1101/2020.05.22.20109900

Cited by 3 publications

(9 citation statements)

References 22 publications

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“…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/ ).…”

Section: Resultsmentioning

confidence: 99%

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Estimating underreporting of leprosy in Brazil using a Bayesian approach

Oliveira

Pescarini

et al. 2021

PLoS Negl Trop Dis

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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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Section: Resultsmentioning

confidence: 99%

“…We used R software, geobr package, (MIT license https://ipeagit.github.io/geobr/ ) to create the maps produced in this work to visualise the spatial analysis, see [ 31 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

Estimating underreporting of leprosy in Brazil using a Bayesian approach

Oliveira

Pescarini

et al. 2021

PLoS Negl Trop Dis

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“…4 8 Failure to detect leprosy cases and the subsequent under-reporting of the disease can occur for a variety of reasons, including the low capacity of healthcare services or health professionals to diagnose and register new cases of the disease, lack of specific leprosy programmes and policies, absent or inadequate national disease registries and deficiencies in national or local leprosy programmes. 9 Models predicting the incidence or prevalence of cases of leprosy can facilitate the identification of new or undetected cases and inform health decision-making in respect of the target population for treatment and disease control and prevention actions. 7 Various prognostic modelling studies of leprosy cases have been reported in the literature, but they have been undertaken in a diverse range of settings using distinct approaches to predict cases.…”

Section: Introductionmentioning

confidence: 99%

Forecasting models for leprosy cases: a scoping review protocol

et al. 2022

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IntroductionLeprosy is a neglected tropical disease caused by Mycobacterium leprae that mainly affects the skin, the peripheral nerves, the mucosa of the upper respiratory tract and the eyes. Mathematical models and statistical methodologies could play an important role in decision-making and help maintain the gains in elimination programmes. Various models for predicting leprosy cases have been reported in the literature, but they have different settings and distinct approaches to predicting the cases. This study describes the protocol for a scoping review to identify and synthesise information from studies using models to forecast leprosy cases.Methods and analysisA scoping review methodology will be applied following the Joanna Briggs Institute methodology for scoping reviews and will be reported according to Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Reviews. We will perform a systematic search from when each database started until April 2022 and we will include the following electronic databases: MEDLINE via PubMed, Embase, Cochrane Library and Latin American and Caribbean Health Science Literature Database. Data will be extracted and recorded on a calibrated predefined data form and will be presented in a tabular form accompanied by a descriptive summary. The Prediction Model Study Risk of Bias Assessment Tool (PROBAST) will be used.Ethics and disseminationNo ethical approval is required for this study. This scoping review will identify and map the methodological and other characteristics of modelling studies predicting leprosy cases. We hope that the review will contribute to scientific knowledge in this area and act as a basis for researchers designing and conducting leprosy models. This information can also be used to enhance national surveillance systems and to target specific policies. The protocol and consequent publications of this scoping review will be disseminated through peer-reviewed publications and policy briefs.Systematic review registrationThis scoping review was registered in the Open Science Framework (https://doi.org/10.17605/OSF.IO/W9375).

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“…Nonetheless, some countries are still facing some challenges in developing a mathematical model that provides an estimate of undetected incident cases that can approximate the reality of cases (4,8). Undetected cases or underreporting cases of leprosy can occur for many reasons, including the low capacity of health care services or health professionals to diagnose and register new cases of the disease, lack of speci c leprosy programs and policies, absence or poor national disease registries, or de ciencies of national or local leprosy programs (9). Models predicting the incidence or prevalence of cases of leprosy can facilitate the identi cation of new or undetected cases and inform health decision-making regarding the target population for treatment and disease control, and prevention actions (7).…”

Section: Introductionmentioning

confidence: 99%

“…Models predicting the incidence or prevalence of cases of leprosy can facilitate the identi cation of new or undetected cases and inform health decision-making regarding the target population for treatment and disease control, and prevention actions (7). Several forecasting modeling studies of leprosy cases have been reported in the literature, however, there are distinct approaches and settings for predicting the cases (9)(10)(11)(12)(13).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Modeling Studies For Predicting New Leprosy Cases: Protocol For A Scoping Review

Ascef

Oliveira

Coriolano

et al. 2021

Preprint

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Background: Leprosy is a neglected tropical disease caused by Mycobacterium leprae that mainly affects the skin, the peripheral nerves, mucosa of the upper respiratory tract, and the eyes. Mathematical models and statistical methodology could have a role to play in decision-making for maintaining the gains in elimination programs. Diverse forecasting modeling studies of leprosy cases have been reported in the literature, but there are distinct approaches and settings for predicting the cases being proposed. The purpose of this study is to complete a scoping review to identify and synthesize forecasting modeling studies of leprosy cases. Methods: A scoping review methodology will be applied following the Joanna Briggs Institute methodology for scoping reviews and will be reported according to PRISMA-ScR. We will perform a systematic search from inception until June 2021 and we will include the following electronic databases: MEDLINE via PubMed, Embase, Cochrane Library, and Lilacs. Data will be extracted and recorded on a calibrated predefined data form and will be presented in a tabular form accompanied by a descriptive summary. The Prediction model study Risk of Bias Assessment Tool (PROBAST) will be used. Discussion: This scoping review will identify and map the methodological characteristics and further evidence from modeling studies for predicting leprosy cases. Thereby contributing to a scientific basis for researchers to inform, design, and conduct appropriate models for predicting cases of leprosy. This information could be used to enhance national surveillance systems and to target specific policies. Systematic review registration: This scoping review was registered in the Open Science Framework (https://doi.org/10.17605/OSF.IO/W9375).

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Estimating Under Reporting of Leprosy in Brazil using a Bayesian Approach

Cited by 3 publications

References 22 publications

Estimating underreporting of leprosy in Brazil using a Bayesian approach

Estimating underreporting of leprosy in Brazil using a Bayesian approach

Forecasting models for leprosy cases: a scoping review protocol

Forecasting Modeling Studies For Predicting New Leprosy Cases: Protocol For A Scoping Review

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