SINAN FTP Database

Available diseases and years to download

[1]:

from pysus.ftp.databases.sinan import SINAN
sinan = SINAN().load() # Loads the files from DATASUS

[2]:

sinan.metadata

[2]:

{'long_name': 'Doenças e Agravos de Notificação',
 'source': 'https://portalsinan.saude.gov.br/',
 'description': 'The Notifiable Diseases Information System - Sinan is primarilyfed by the notification and investigation of cases of diseases and conditions listed in the national list of compulsorily notifiable diseases (Consolidation Ordinance No. 4, September 28, 2017, Annex).However, states and municipalities are allowed to include other important health problems in their region, such as difilobotriasis in the municipality of São Paulo. Its effective use enables the dynamic diagnosis of the occurrence of an event in the population, providing evidence for causal explanations of compulsorily notifiable diseases and indicating risks to which people are exposed. This contributes to identifying the epidemiological reality of a specific geographical area. Its systematic, decentralized use contributes to the democratization of information, allowing all healthcare professionals to access and make it available to the community. Therefore, it is a relevant tool to assist in health planning, define intervention priorities, and evaluate the impact of interventions.'}

Listing SINAN Codes & Diseases

[3]:

sinan.diseases

[3]:

{'ACBI': 'Acidente de trabalho com material biológico',
 'ACGR': 'Acidente de trabalho',
 'ANIM': 'Acidente por Animais Peçonhentos',
 'ANTR': 'Atendimento Antirrabico',
 'BOTU': 'Botulismo',
 'CANC': 'Cancêr relacionado ao trabalho',
 'CHAG': 'Doença de Chagas Aguda',
 'CHIK': 'Febre de Chikungunya',
 'COLE': 'Cólera',
 'COQU': 'Coqueluche',
 'DENG': 'Dengue',
 'DERM': 'Dermatoses ocupacionais',
 'DIFT': 'Difteria',
 'ESQU': 'Esquistossomose',
 'EXAN': 'Doença exantemáticas',
 'FMAC': 'Febre Maculosa',
 'FTIF': 'Febre Tifóide',
 'HANS': 'Hanseníase',
 'HANT': 'Hantavirose',
 'HEPA': 'Hepatites Virais',
 'IEXO': 'Intoxicação Exógena',
 'INFL': 'Influenza Pandêmica',
 'LEIV': 'Leishmaniose Visceral',
 'LEPT': 'Leptospirose',
 'LERD': 'LER/Dort',
 'LTAN': 'Leishmaniose Tegumentar Americana',
 'MALA': 'Malária',
 'MENI': 'Meningite',
 'MENT': 'Transtornos mentais relacionados ao trabalho',
 'NTRA': 'Notificação de Tracoma',
 'PAIR': 'Perda auditiva por ruído relacionado ao trabalho',
 'PEST': 'Peste',
 'PFAN': 'Paralisia Flácida Aguda',
 'PNEU': 'Pneumoconioses realacionadas ao trabalho',
 'RAIV': 'Raiva',
 'SDTA': 'Surto Doenças Transmitidas por Alimentos',
 'SIFA': 'Sífilis Adquirida',
 'SIFC': 'Sífilis Congênita',
 'SIFG': 'Sífilis em Gestante',
 'SRC': 'Síndrome da Rubéola Congênia',
 'TETA': 'Tétano Acidental',
 'TETN': 'Tétano Neonatal',
 'TOXC': 'Toxoplasmose Congênita',
 'TOXG': 'Toxoplasmose Gestacional',
 'TRAC': 'Inquérito de Tracoma',
 'TUBE': 'Tuberculose',
 'VARC': 'Varicela',
 'VIOL': 'Violência doméstica, sexual e/ou outras violências',
 'ZIKA': 'Zika Vírus'}

Getting specific files

[4]:

sinan.get_files(dis_code=["ZIKA", "CHIK"])

[4]:

[CHIKBR15.dbc,
 CHIKBR16.dbc,
 CHIKBR17.dbc,
 CHIKBR18.dbc,
 CHIKBR19.dbc,
 CHIKBR20.dbc,
 CHIKBR21.dbc,
 CHIKBR22.dbc,
 CHIKBR23.dbc,
 ZIKABR16.dbc,
 ZIKABR17.dbc,
 ZIKABR18.dbc,
 ZIKABR19.dbc,
 ZIKABR20.dbc,
 ZIKABR21.dbc,
 ZIKABR22.dbc,
 ZIKABR23.dbc]

[5]:

sinan.get_files(year=2023)

[5]:

[BOTUBR23.dbc,
 CHIKBR23.dbc,
 DENGBR23.dbc,
 ESQUBR23.dbc,
 FTIFBR23.dbc,
 HANSBR23.dbc,
 MENIBR23.dbc,
 TOXCBR23.dbc,
 TOXGBR23.dbc,
 TUBEBR23.dbc,
 VARCBR23.dbc,
 ZIKABR23.dbc]

[6]:

sinan.get_files(dis_code=["DENG", "ZIKA"], year=[2022, 2023])

[6]:

[DENGBR22.dbc, DENGBR23.dbc, ZIKABR22.dbc, ZIKABR23.dbc]

[7]:

files = sinan.get_files(dis_code="BOTU", year=[2007, 2008])
botu_2007, botu_2008 = files

Describing a file inside DATASUS server

[8]:

sinan.describe(botu_2007)

[8]:

{'name': 'BOTUBR07.dbc',
 'disease': 'Botulismo',
 'year': 2007,
 'size': '7.5 kB',
 'last_update': '2021-11-23 11:55AM'}

[9]:

sinan.describe(botu_2008)

[9]:

{'name': 'BOTUBR08.dbc',
 'disease': 'Botulismo',
 'year': 2008,
 'size': '8.3 kB',
 'last_update': '2021-11-23 11:56AM'}

Downloading files

You can rather download multiple files or download them individually:

[10]:

sinan.download(files) # Downloads to default directory

100%|████████████████████████████| 8.35k/8.35k [00:00<00:00, 6.12MB/s]

[10]:

[/home/bida/pysus/BOTUBR07.parquet, /home/bida/pysus/BOTUBR08.parquet]

[11]:

botu_2007.download() # or specify a custom directory with `local_dir=`

[11]:

/home/bida/pysus/BOTUBR07.parquet

@Note: If the file has been downloaded already, it’s required to delete it in order to download the lastest updated file from DATASUS.

Reading files

PySUS uses Parquets as output, use the method to_dataframe() to read the file as pandas DataFrame

[12]:

parquet = botu_2007.download()

[13]:

parquet.to_dataframe()

[13]:

	TP_NOT	ID_AGRAVO	DT_NOTIFIC	SEM_NOT	NU_ANO	SG_UF_NOT	ID_MUNICIP	ID_REGIONA	ID_UNIDADE	DT_SIN_PRI	...	NU_PROTEI	DT_LIQUOR	TP_SENSITI	TP_MOTORA	TP_REPETE	DS_ALI1	DS_LOCAL1	DT_ENCERRA
0	2	A051	2007-01-12	200702	2007	41	410690	1356	2384299	2006-12-31	...	53	20070112	2	2	3			20070222
1	2	A051	2007-01-12	200702	2007	35	355030	1331	2077485	2007-01-10	...						TORTA DE PALMITO/ FRANGO .		20070323
2	2	A051	2007-01-03	200701	2007	52	521180	1791	2381532	2006-11-16	...								20070515
3	2	A051	2007-01-08	200702	2007	43	431410	1611	2246988	2007-01-03	...	31	20070107				SALAME	DOMICILIO
4	2	A051	2007-02-27	200709	2007	50	500830	1973	2757206	2007-02-27	...								20070816
5	2	A051	2007-02-16	200707	2007	35	354340	1348	2082187	2006-12-12	...			1	2	2
6	2	A051	2007-02-15	200707	2007	33	330170		2290227	2007-02-12	...	27	20070214						20070724
7	2	A051	2007-02-28	200709	2007	22	220800	1888	4009622	2007-02-28	...								20070503
8	2	A051	2007-05-12	200719	2007	27	270710	1539	2721643	2007-05-09	...								20070716
9	2	A051	2007-06-11	200724	2007	35	354850	1349	2025752	2007-06-11	...								20071120
10	2	A051	2007-06-22	200725	2007	35	352340	1342	2023709	2007-06-12	...	40	20070616						20070704
11	2	A051	2007-06-27	200726	2007	29	292740	1380	0004057	2007-06-25	...	39	20070625	2	2				20070730
12	2	A051	2007-07-30	200731	2007	53	530010		0010456	2007-07-26	...	83	20070728	1	2	2			20070730
13	2	A051	2007-07-10	200728	2007	52	520870	1779	2338262	2007-05-08	...								20070814
14	2	A051	2007-08-28	200735	2007	35	354850	1349	2079720	2007-08-17	...						SALGADO TORTA PAO COM QUEIJO C	CASA DE MASSAS ROMANA EM SANTO	20070903
15	2	A051	2007-08-24	200734	2007	35	354850	1349	2079720	2007-08-17	...		20070819						20071025
16	2	A051	2007-08-17	200733	2007	35	355030	1331	2077574	2007-08-12	...	40	20070813
17	2	A051	2007-08-20	200734	2007	51	510340	1578	2495015	2007-08-18	...								20070918
18	2	A051	2007-08-23	200734	2007	23	230440	1519	2482169	2007-08-19	...						MORTADELA	DOMICILIO	20071023
19	2	A051	2007-09-16	200738	2007	31	317010	1461	2206595	2007-09-08	...		20070915						20071017
20	2	A051	2007-09-06	200736	2007	35	354340	1348	2082187	2007-08-31	...
21	2	A051	2007-09-04	200736	2007	35	355220	1353	2081695	2007-08-31	...
22	2	A051	2007-10-08	200741	2007	35	355220	1353	2081695	2007-10-08	...
23	2	A051	2007-10-02	200740	2007	31	314330	1473	2149990	2007-09-28	...	19	20070930						20080601
24	2	A051	2007-11-06	200745	2007	35	355220	1353	2078732	2007-11-03	...		20071105						20080108
25	2	A051	2007-11-01	200744	2007	52	520870	1779	2518406	2007-10-02	...			1	1	1			20071112

26 rows × 140 columns