Individual-level hospital admission records from Brazil's Hospital Information System (SIH/SUS), covering 35 years of public hospital data. Each record corresponds to one Reduced Hospital Admission Authorization (AIH Reduzida — RD) and includes patient demographics, diagnoses (ICD-9 for 1992–1997, ICD-10 from 1998 onward), procedures, length of stay, costs, and outcome. Converted from legacy .dbc files to Apache Parquet.

This is the RD (AIH Reduzida) sub-module of the sih/ namespace. The companion SP (Serviços Profissionais) sub-module — sih-sp — has one row per act/procedure performed inside each admission and joins to this dataset via SP_NAIH = N_AIH.

Part of the healthbr-data project — open redistribution of Brazilian public health data.

Summary

Item	Detail
Official source	DATASUS FTP / Ministry of Health
Temporal coverage	1992–2026
Geographic coverage	All 27 Brazilian states (by municipality of hospitalization)
Granularity	Individual: one row per hospital admission (AIH)
Volume	415M+ records (11,011 .dbc files processed)
Format	Apache Parquet, partitioned by `ano/mes/uf`
Data types	All fields stored as `string` (preserves original format)
Update frequency	Monthly (source publishes ~2–3 months after competency month)
License	CC-BY 4.0

Resumo em português

SIH RD — Registros de Internações Hospitalares (Brasil, 1992–2026)

Microdados individuais de internações hospitalares do Sistema de Informações Hospitalares do SUS (SIH/SUS), cobrindo 35 anos de dados hospitalares públicos. Cada registro corresponde a uma Autorização de Internação Hospitalar Reduzida (AIH-RD) e inclui dados demográficos do paciente, diagnósticos (CID-9 para 1992–1997, CID-10 a partir de 1998), procedimentos, tempo de permanência, custos e desfecho. Convertidos de arquivos .dbc legados para Apache Parquet. Este é o submódulo RD do namespace sih/; o submódulo SP (atos/procedimentos por internação, chave SP_NAIH = N_AIH) está em sih-sp.

Item	Detalhe
Fonte oficial	FTP DATASUS / Ministério da Saúde
Cobertura temporal	1992–2026
Cobertura geográfica	Todos os 27 estados brasileiros (por município de internação)
Granularidade	Individual: uma linha por internação hospitalar (AIH)
Volume	415M+ registros (11.011 arquivos .dbc processados)
Formato	Apache Parquet, particionado por `ano/mes/uf`
Atualização	Mensal (fonte publica ~2–3 meses após o mês de competência)

Para documentação completa em português, consulte o repositório do projeto.

Data access

Data is hosted on Cloudflare R2 and accessed via S3-compatible API. The credentials below are read-only and intended for public use.

R (Arrow)

library(arrow)
library(dplyr)

Sys.setenv(
  AWS_ENDPOINT_URL      = "https://5c499208eebced4e34bd98ffa204f2fb.r2.cloudflarestorage.com",
  AWS_ACCESS_KEY_ID     = "28c72d4b3e1140fa468e367ae472b522",
  AWS_SECRET_ACCESS_KEY = "2937b2106736e2ba64e24e92f2be4e6c312bba3355586e41ce634b14c1482951",
  AWS_DEFAULT_REGION    = "auto"
)

# Open a single partition (year/month/state)
ds <- open_dataset(
  "s3://healthbr-data/sih/rd/ano=2024/mes=01/uf=SP/",
  format = "parquet"
)

# Example: hospital admissions in São Paulo, Jan 2024, by diagnosis
ds |>
  collect() |>
  count(DIAG_PRINC, sort = TRUE) |>
  head(20)

Important: Point to specific partitions (ano=YYYY/mes=MM/uf=XX/), not to the dataset root. The root contains README.md and manifest.json, which Arrow cannot read as Parquet files — and never open sih/ itself, which also holds the sp/ sub-module with a different schema. You can also open broader paths like ano=YYYY/ or ano=YYYY/mes=MM/ to load multiple partitions at once.

Python (PyArrow)

import pyarrow.dataset as pds
import pyarrow.fs as fs

s3 = fs.S3FileSystem(
    endpoint_override="https://5c499208eebced4e34bd98ffa204f2fb.r2.cloudflarestorage.com",
    access_key="28c72d4b3e1140fa468e367ae472b522",
    secret_key="2937b2106736e2ba64e24e92f2be4e6c312bba3355586e41ce634b14c1482951",
    region="auto"
)

# Single partition (year/month/state)
dataset = pds.dataset(
    "healthbr-data/sih/rd/ano=2024/mes=01/uf=SP/",
    filesystem=s3,
    format="parquet"
)

# Example: admissions in São Paulo, Jan 2024
df = dataset.to_table().to_pandas()
print(df.head())
print(f"Records: {len(df)}, Columns: {len(df.columns)}")

Note: These credentials are read-only and safe to use in scripts. The bucket does not allow anonymous S3 access — credentials are required. Point to specific partitions, not the dataset root (see note above).

File structure

s3://healthbr-data/sih/
  README.md            ← namespace index (rd/, sp/)
  rd/                  ← this dataset
    README.md
    manifest.json
    ano=1992/
      mes=01/
        uf=AC/
          part-0.parquet
        uf=AL/
          part-0.parquet
        ...
      mes=02/
        ...
    ...
    ano=2026/
      mes=01/
        ...
  sp/                  ← Serviços Profissionais (see sih-sp)
    ...

Until 2026-08-17 this dataset lived directly under sih/ano=…. It was moved to sih/rd/ when the SP sub-module was added; update old paths.

Historical schemas

The AIH form underwent major revisions from 1992 to 2015. The dataset preserves 14 distinct schemas:

Period	Columns	Key characteristics
1992–1993	35	Start of computerization; ICD-9; 2-digit year
1994	39
1995–1997	41–42	ICD-9; dates as YYMMDD
1998	41	Transition: ICD-10, 8-digit dates, 4-digit year
1999–2001	52–60	+ICU fields, +management fields
2002–2005	68–69	+CNES (2004)
2006–2007	75
2008–2010	86	FTP era change; SIGTAP 10-digit procedures; +RACA_COR
2011–2012	93
2013	95
2014–2026	113	Stabilized — +DIAGSEC1–DIAGSEC9

The number of columns varies by year. Columns not present in a given era will be absent from that partition's Parquet file. Use open_dataset(unify_schemas = TRUE) in Arrow to query across eras (missing columns filled with null).

Schema (modern era, 2014–2026, 113 columns)

Key variables in the most recent schema:

Variable	Description
`UF_ZI`	State code (processing)
`ANO_CMPT`	Competency year
`MES_CMPT`	Competency month
`N_AIH`	AIH number (admission ID)
`CNES`	Health facility code (CNES)
`MUNIC_MOV`	Municipality of hospitalization (IBGE code)
`MUNIC_RES`	Patient's municipality of residence (IBGE code)
`NASC`	Patient's date of birth
`SEXO`	Sex (1=male, 3=female)
`IDADE`	Age
`RACA_COR`	Race/color
`DT_INTER`	Admission date
`DT_SAIDA`	Discharge date
`DIAS_PERM`	Length of stay (days)
`DIAG_PRINC`	Primary diagnosis (ICD-10)
`DIAGSEC1`–`DIAGSEC9`	Secondary diagnoses (ICD-10)
`PROC_REA`	Procedure performed (SIGTAP code)
`VAL_TOT`	Total amount (R$)
`MORTE`	Death during admission (0=no, 1=yes)
`CEP`	Patient's ZIP code
`CAR_INT`	Admission type
`COMPLEX`	Complexity level

For the complete variable list across all 14 schemas, see the exploration document.

Source and processing

Original source: 11,011 .dbc files from the DATASUS FTP server, covering two directories: 200801_/Dados/ (modern era, 5,856 files) and 199201_200712/Dados/ (legacy era, 5,155 files). Scope: RD (AIH Reduzida) — SP is published as a separate sub-module (sih/sp/); RJ, ER are future expansions.

Processing: .dbc → R (read.dbc::read.dbc()) → all fields cast to character → Parquet (arrow::write_parquet()) → upload to R2 (rclone). No value transformations are applied — field values are published exactly as provided by the Ministry of Health. Dates in the legacy era (YYMMDD) are preserved as-is; ICD-9 codes (1992–1997) are not converted to ICD-10.

Bootstrap: Processed in 2 sprints: Sprint 1 (2008–2026, 5,856 files, 217.8M records, ~18h) + Sprint 2 (1992–2007, 5,155 files, 197.6M records, ~12–15h). Total: 11,011 files, 415,372,502 records, 16.1 GiB on R2.

Reproducibility & provenance

Every Parquet file written by pipeline version ≥ 1.1.0 carries a JSON provenance record in its schema metadata (key healthbr): source_url, source_file, source_hash_md5, source_size_bytes, download_date, pipeline_script, pipeline_version and (≥ 1.2.0) git_commit. The same facts are recorded per partition in manifest.json (plus SHA-256 and record count of each output file) and per source file in the version-control CSV in the GitHub repository. Together they let anyone re-derive a partition from the Ministry's original file and the exact code commit, and verify that the copy they hold is intact. Data are not versioned: the R2 copy is the latest publication; revisions by the Ministry replace files. Files from the initial bootstrap (pipeline 1.0.0, 2026-03) received the record afterwards by a metadata-only rewrite (2026-08-18): same content, pipeline_version kept as 1.0.0, git_commit inferred from the repository history and flagged git_commit_inferred: true (see metadata_backfill inside the record). Full policy and audit recipe: docs/policy-reproducibility-pt.md.

arrow::read_parquet("part-0.parquet", as_data_frame = FALSE)$metadata$healthbr

Known limitations

Government data, not ours. Values are preserved exactly as in the original .dbc files, including any inconsistencies or missing data.
Fourteen historical schemas. The number of columns varies from 35 (1992) to 113 (2014+). Queries spanning multiple eras must handle missing columns.
All fields are strings. Numeric fields (costs, age, length of stay) and dates must be parsed by the user.
RD only. This dataset contains only AIH Reduzida (processed admissions). Professional services (SP) are a separate sub-module (sih/sp/, sih-sp); RJ (rejected) and ER (errors) are not included.
ICD-9 in legacy era (1992–1997). Diagnosis codes use ICD-9 (6 characters) before 1998 and ICD-10 (3–4 characters) from 1998 onward. No conversion is applied.
Legacy date format (1992–1997). Dates use YYMMDD (6 digits) in the legacy era and YYYYMMDD (8 digits) from 1998 onward. No conversion is applied.
19 historical gaps. 19 files from the legacy era were not found on the FTP server (mostly Roraima 1995–2000, plus AC 1994 and AP 2007). These are Ministry-side gaps, not pipeline failures.
Monthly partitioning. Unlike SINASC (annual), SIH is partitioned by year/month/state, reflecting the monthly publication frequency.

Related datasets

Dataset	Period	Records	Link
SIH SP (professional services / acts per admission)	1997–present	~3,199M	sih-sp
SINASC (live births)	1994–2022	85M+	sinasc
SI-PNI Microdados (vaccination)	2020–present	736M+	sipni-microdados
SI-PNI COVID (vaccination)	2021–present	608M+	sipni-covid
SI-PNI Agregados — Doses	1994–2019	84M+	sipni-agregados-doses
SI-PNI Agregados — Cobertura	1994–2019	2.8M+	sipni-agregados-cobertura
SI-PNI Dicionários	Static	263 rows	sipni-dicionarios

Citation

@misc{healthbrdata,
  author = {Sidney da Silva Bissoli},
  title  = {healthbr-data: Redistribution of Brazilian Public Health Data},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SidneyBissoli/sih-rd},
  note   = {Original source: Ministry of Health / DATASUS}
}

Contact

GitHub: https://github.com/SidneyBissoli
Hugging Face: https://huggingface.co/SidneyBissoli
E-mail: sbissoli76@gmail.com

Last updated: 2026-08-17 (moved to sih/rd/; SP sub-module added)

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