@article{fdi:010067771,
  title = {{A}ccuracy of the malaria epidemiological surveillance system data in the state of {A}mazonas},
  author = {{W}iefels, {A}. and {W}olfarth-{C}outo, {B}. and {F}ilizola, {N}. and {D}urieux, {L}aurent and {M}angeas, {M}organ},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {E}pidemiological {S}urveillance {S}ystem for {M}alaria ({SIVEP}-{M}alaria) is the {B}razilian governmental program that registers all information about compulsory reporting of detected cases of malaria by all medical units and medical practitioners. {T}he objective of this study is to point out the, main sources of errors in the {SIVEP}-{M}alaria database by applying a data cleaning method to assist researchers about the best way to use it and to report the problems to authorities. {T}he aim of this study was to assess the quality of the data collected by the surveillance system and its accuracy. {T}he {SIVEP}-{M}alaria data base used was for the state of {A}mazonas, {B}razil, with data collected from 2003 to 2014. {A} data cleaning method was applied to the database to detect and remove erroneous records. {I}t was observed that the collecting procedure of the database is not homogeneous among the municipalities and over the years. {S}ome of the variables had different data collection periods, missing data, outliers and inconsistencies. {V}ariables depending on the health agents showed a good quality but those that rely on patients were often inaccurate. {W}e showed that a punctilious preprocessing is needed to produce statistically correct data from the {SIVEP}-{M}alaria data base. {F}ine spatial scale and multi-temporal analysis are of particular concern due to the local concentration of uncertainties and the data collecting seasonality observed. {T}his assessment should help to enhance the quality of studies and the monitoring of the use of the {SIVEP} database.},
  keywords = {{E}rroneous data ; {D}atabase ; {H}ealth surveillance ; {BRESIL} ; {AMAZONAS}},
  booktitle = {},
  journal = {{A}cta {A}mazonica},
  volume = {46},
  numero = {4},
  pages = {383--390},
  ISSN = {0044-5967},
  year = {2016},
  DOI = {10.1590/1809-4392201600285},
  URL = {https://www.documentation.ird.fr/hor/fdi:010067771},
}