Syphilis Trigram: a domain-specific visualisation to combat syphilis epidemic and improve the quality of maternal and child health in Brazil

Abstract

Background: The Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn.

Methods: The Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system.

Results: The main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system.

Conclusions: The proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time.

Keywords: Congenital syphilis; Data visualization; Poverty; Syphilis.

Fig. 1

Incidence rates of congenital syphilis (per 1,000 live births). Brazil, 2019. Source: Sistema de Informação de Agravo de Notificação (SINAN), updated on 06/30/2020

Fig. 2

Infant mortality coefficient due to congenital syphilis (per 1,000 live births). Brazil, 2019. Source: Sistema de Informação sobre Mortalidade (SIM), updated on 06/30/2020

Fig. 3

Cycles activities. Adapted from McManus [23]

Fig. 4

Software System iterative learning cycles

Fig. 5

Rich picture of PMCP domain. Source: the author

Fig. 6

Visual structure of the Trigram

Fig. 7

Evolution of the Trigram versions. Source: the author

Fig. 8

Mean Trigram expanded with two Mean sub-Trigrams. The Mean Trigram on the left represents pregnancies that have more than one test, and the right one only represents pregnancies with one test. Source: the author

Fig. 9

Reading of the Mean Trigram. Each transverse slice represents one or more Trigrams according to the three analyzed variables. Source: the author

Fig. 10

Comparison between Mean Trigrams of the State of Pernambuco, for different years and the city of Recife

Fig. 11

Complete infographic interface, with Mean Trigram. Source: the author

Fig. 12

Syphilis detection rates in pregnant women according to Federation Unit and capital. Brazil, 2016. Source: SINAN, updated on 06/30/2017

Fig. 13

Congenital syphilis incidence rates by State and capital. Brazil, 2016. Source: SINAN, updated on 06/30/2017

Fig. 14

Congenital syphilis incidence rates by State and capital. Brazil, 2017. Source: SINAN, updated on 06/30/2018

Fig. 15

Syphilis detection rates in pregnant women according to State and capital. Brazil, 2017. Source: SINAN, updated on 06/30/2018

Fig. 16

Mean Trigrams of the State of Pernambuco in 2016. Source: the author

Fig. 17

Mean Trigrams of the State of Pernambuco in 2017. Source: the author