Fig 1.
Identification of the study population.
(A) Exclusion criteria. (B) Linkage between leprosy and cutaneous leishmaniasis (CL) datasets from the Brazilian Notifiable Diseases Information System (SINAN). Data from Mato Grosso state, Brazil, 2008–2017. a Transfers (within the municipality, from another municipality, from another state, or from another country), unknown, or cases reinserted in the system for a new treatment round after abandonment or therapeutic failure.
Table 1.
Frequency distribution of patients diagnosed with leprosy and cutaneous leishmaniasis (CL) according to the time interval until the diagnosis of the second disease and the order of diagnosis.
Data from Mato Grosso state, Brazil, 2008–2017.
Fig 2.
Geographic characterization of the patients diagnosed with leprosy and cutaneous leishmaniasis (CL) in Mato Grosso state, Brazil, 2008–2017.
(A) Represents the absolute number of patients and the cumulative detection coefficient for leprosy and CL in the same individuals per municipality. (B) Represents the local Moran’s Index analysis for the cumulative detection coefficient per municipality. The digital georeferenced database of the municipalities was obtained from the Brazilian Institute of Geography and Statistics (https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2018/UFs/MT/MT.zip).
Fig 3.
Effect of age on the time interval between the diagnosis of leprosy and cutaneous leishmaniasis in the same individuals according to Cox regression model with P-splines.
Solid line represents spline coefficient of each estimated knot while dashed lines are the limits of the 95% confidence intervals. Data from Mato Grosso state, Brazil, 2008–2017.
Table 2.
Cox proportional hazards models for the time elapsed between the diagnosis of leprosy and cutaneous leishmaniasis in the same individuals.
Data from Mato Grosso state, Brazil, 2008–2017.