Leprosy transmission: still a challenge

A novel integrated molecular and serological analysis method to predict new cases of leprosy amongst household contacts

Background

Early detection of Mycobacterium leprae is a key strategy for disrupting the transmission chain of leprosy and preventing the potential onset of physical disabilities. Clinical diagnosis is essential, but some of the presented symptoms may go unnoticed, even by specialists. In areas of greater endemicity, serological and molecular tests have been performed and analyzed separately for the follow-up of household contacts, who are at high risk of developing the disease. The accuracy of these tests is still debated, and it is necessary to make them more reliable, especially for the identification of cases of leprosy between contacts. We proposed an integrated analysis of molecular and serological methods using artificial intelligence by the random forest (RF) algorithm to better diagnose and predict new cases of leprosy.

Methods

The study was developed in Governador Valadares, Brazil, a hyperendemic region for leprosy. A longitudinal study was performed, including new cases diagnosed in 2011 and their respective household contacts, who were followed in 2011, 2012, and 2016. All contacts were diligently evaluated by clinicians from Reference Center for Endemic Diseases (CREDEN-PES) before being classified as asymptomatic. Samples of slit skin smears (SSS) from the earlobe of the patients and household contacts were collected for quantitative polymerase chain reaction (qPCR) of 16S rRNA, and peripheral blood samples were collected for ELISA assays to detect LID-1 and ND-O-LID.

Results

The statistical analysis of the tests revealed sensitivity for anti-LID-1 (63.2%), anti-ND-O-LID (57.9%), qPCR SSS (36.8%), and smear microscopy (30.2%). However, the use of RF allowed for an expressive increase in sensitivity in the diagnosis of multibacillary leprosy (90.5%) and especially paucibacillary leprosy (70.6%). It is important to report that the specificity was 92.5%.

Conclusion

The proposed model using RF allows for the diagnosis of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.

Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae) that can infect cells in the skin and nerves. Despite efforts to eliminate leprosy, the number of M. leprae infected individuals who develop leprosy is still substantial in the world. The diagnosis relies mainly on clinical parameters. Histopathological and bacteriological analysis help to classify clinical forms of patients. Serology and polymerase chain reaction (PCR) assays are claimed by health professionals as auxiliary tools, but until now these tests have been used almost exclusively in research, with minor use in leprosy reference centers throughout Brazil. Here, we tested quantitative PCR (qPCR) designed to amplify specific M. leprae targets and ELISA assays to detect antibody response to recombinant antigens (LID-1, ND-O-LID). All results were analyzed by multivariate analysis based in artificial intelligence. We chose random forest as a classification algorithm to aid in the diagnosis and the monitoring of contacts. The results allowed us to diagnose cases of leprosy with high sensitivity and specificity and the early identification of new cases among household contacts.

Socio-economic and environmental effects influencing the development of leprosy in Bahia, north-eastern Brazil

OBJECTIVES

To investigate spatial clusters and possible associations between relative risks of leprosy with socio-economic and environmental factors, taking into account diagnosed cases in children under 15 years old.

METHODS

An ecological study was conceived using data aggregated by municipality to identify possible spatial clusters of leprosy from 2005 to 2011. Relative risks were calculated accounting for the respective covariate gender. The second stage of the analysis consisted of verifying possible associations between the relative risks of leprosy as a dependent variable, and socio-economic and environmental variables as independent. This was performed using a multivariate regression analysis according to a previously defined conceptual framework.

RESULTS

Overall rates have decreased from 0.88/10 000 in 2005 to 0.52 in 2011. Spatial scan statistics identified 4 high-risk and 6 low-risk clusters. In the regression model, after allowing for spatial dependence, relative risks were associated with higher percentage of water bodies, higher Gini index, higher percentage of urban population, larger average number of dwellers by permanent residence and smaller percentage of residents born in Bahia.

CONCLUSIONS

Although relative risks of leprosy in Bahia have been decreasing, they remain very high. The association between relative risks of leprosy and water bodies in the proposed geographic scale indicates that hypothesis linking M. leprae and humid environments cannot be discarded. Socio-economic conditions such as inequality, a greater number of dwellers by residence and migration are derived from the urbanisation process carried out in this State. Precarious settlements and poor living conditions in the cities would favour the continuity of leprosy transmission.

Lepromatous leprosy and perianal tuberculosis: a case report and literature review

Leprosy is a chronic infectious disease caused by Mycobacterium leprae, a microorganism that usually affects skin and nerves. Although it is usually well-controlled by multidrug therapy (MDT), the disease may be aggravated by acute inflammatory reaction episodes that cause permanent tissue damage particularly to peripheral nerves. Tuberculosis is predominantly a disease of the lungs; however, it may spread to other organs and cause an extrapulmonary infection. Both mycobacterial infections are endemic in developing countries including Brazil, and cases of coinfection have been reported in the last decade. Nevertheless, simultaneous occurrence of perianal cutaneous tuberculosis and erythema nodosum leprosum is very rare, even in countries where both mycobacterial infections are endemic.