ABSTRACT
OBJECTIVE
To present a protocol to criticize data on fluoride monitoring in water with R software programming features, illustrating its application to describe data coverage, and fluoridation quality in 2015.
METHODS
The study used big data from the Sistema de Informação de Vigilância da Qualidade da Água para Consumo Humano (Information System for Surveillance of Water Quality for Human Consumption) that included all the Brazilian municipalities. Data criticism procedures were performed with the aid of R software. Filters were applied to remove municipalities with less than four months of records (1), and records with null values (2) and outliers (3). Municipalities were classified regarding the presence of valid information and fluoridation quality according to macro-region, federation units, and population size, presenting the roadmap at each step.
RESULTS
Approximately 134,000 records were reviewed. Of the Brazilian municipalities, 39% had data on the fluoride parameter, and only 33.3% had four months or more of information frequency. After applying filters, 1,810 (32.5%) municipalities had valid information for the fluoride parameter, with substantial variation between the South (83.6%) and North (0.7%) macro-regions. Of these, 726 (40.1%) showed very good fluoridation quality, determined by 80% or more records within the optimal concentration interval for prevention of dental caries, with higher value (54.3%) in municipalities with 50,000 inhabitants or more, and lower (34.2%) in those with less than 10,000 inhabitants.
CONCLUSIONS
Important differences persist within and between the Brazilian macro-regions regarding both the availability of information on the parameter, and the quality of water fluoridation in public supply systems in Brazil. The protocol for data review and processing with R software programming resources proved to be very useful for the production of information for decision-making based on a standardized method.
Water Supply; Fluoridation, standards; Water Supply Coverage; Water Quality; Big Data; Decision Making, Organizational
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