# sih-br-mcp, microdatasus or PySUS? One question about Brazilian hospital admissions, three ways Sidney Bissoli 2026-11-02 - [The question, and why it needs 432 files](#the-question-and-why-it-needs-432-files) - [Three ways](#three-ways) - [microdatasus (R)](#microdatasus-r) - [PySUS (Python)](#pysus-python) - [sih-br-mcp (inside the assistant)](#sih-br-mcp-inside-the-assistant) - [The trap in the middle: PySUS 2.11.7 and the files without a group](#the-trap-in-the-middle-pysus-2117-and-the-files-without-a-group) - [Volume, in numbers](#volume-in-numbers) - [What sih-br-mcp does not do](#what-sih-br-mcp-does-not-do) - [Which one](#which-one) *Published 2 November 2026. Every number here was measured that day: sih-br-mcp 1.8.1 on the public endpoint, microdatasus 3.0.0 with dplyr 1.2.1 and R 4.6.1 on Windows, PySUS 2.11.7 with Python 3.13 on Linux, over a residential connection in Brazil. The scripts are in [`scripts/comparativo/`](https://github.com/SidneyBissoli/sih-br-mcp/tree/master/scripts/comparativo) of the sih-br-mcp repository; nothing is illustrative. The same comparison, as a product page: [sih.sidneybissoli.com/vs-microdatasus-pysus](https://sih.sidneybissoli.com/vs-microdatasus-pysus).* If you work with Brazilian public hospital admissions — SIH/SUS, the AIH records DATASUS publishes every month — you almost certainly reach the data through one of two packages: [microdatasus](https://github.com/rfsaldanha/microdatasus), by Raphael Saldanha, in R, or [PySUS](https://github.com/AlertaDengue/PySUS), by the AlertaDengue team, in Python. Both download and decode the **microdata**: one record per admission. [sih-br-mcp](https://sih.sidneybissoli.com) is mine and sits one layer up: an MCP server that answers the **aggregate question** inside an AI assistant (Claude, ChatGPT, any MCP client) from pre-built cubes, with provenance in every answer. They are tools for different jobs, which is exactly why it is worth putting all three in front of the same question. ## The question, and why it needs 432 files > How many pneumonia admissions (ICD-10 J12–J18) did residents of > Espírito Santo have in 2024, and how many deaths? It looks like one file. DATASUS publishes SIH as one file per **hospital state** and **billing month** (the month the AIH was billed, not the month of admission). The question is by state of **residence** and **admission date**. Two consequences: - A resident of Espírito Santo admitted in Minas Gerais is in Minas Gerais’s file. - An admission in December 2024 may be billed in March 2025; the four following billing months close 99.7–99.9% of a year. The honest answer from microdata: 27 states × 16 billing months (2024-01 to 2025-04) = **432 files, 1,358 MiB of `.dbc`, 18,863,265 records**. The natural shortcut, reading only Espírito Santo’s 16 files (30 MiB), gives **13,097** admissions. It misses 208: residents admitted in Minas Gerais (102), Rio de Janeiro (83) and elsewhere. Small (1.6%), and exactly the kind of difference nobody notices. ## Three ways | | Admissions | Deaths | Downloaded | Time | |:---------------------------|-----------:|-------:|:---------------------|-----------:| | **sih-br-mcp** 1.8.1 | 13,305 | 1,250 | 21 KB response | 0.9 s | | microdatasus 3.0.0 | 13,305 | 1,250 | 432 files, 1,358 MiB | 40.6 min | | PySUS 2.11.7, `group="RD"` | 9,135 | 760 | 345 of 432 files | 59.4 min\* | \* On Linux under WSL, reading and writing the Windows disk: not comparable with microdatasus’s time. microdatasus and sih-br-mcp reach **the same number, group by group**: J12 461, J13 37, J14 1, J15 3,648, J16 265, J17 100, J18 8,793. That is neither coincidence nor anyone’s merit: sih-br-mcp’s cubes are built from a 1:1 Parquet mirror of the same `.dbc` files, over the same billing-month window. What differs is **where the work happens**. With microdata, on your machine, for every question. With sih-br-mcp, once, when the cubes are built; the question only reads the result. PySUS fell short for a reason that gets its own section below. ### microdatasus (R) One file at a time: binding all 432 does not fit in memory. ``` r library(microdatasus) library(dplyr) ufs <- c("AC", "AL", "AM", "AP", "BA", "CE", "DF", "ES", "GO", "MA", "MG", "MS", "MT", "PA", "PB", "PE", "PI", "PR", "RJ", "RN", "RO", "RR", "RS", "SC", "SE", "SP", "TO") comps <- seq(as.Date("2024-01-01"), as.Date("2025-04-01"), by = "month") pneu <- list() for (d in as.list(comps)) for (uf in ufs) { y <- as.integer(format(d, "%Y")); m <- as.integer(format(d, "%m")) rd <- fetch_datasus(year_start = y, month_start = m, year_end = y, month_end = m, uf = uf, information_system = "SIH-RD", vars = c("MUNIC_RES", "DT_INTER", "DIAG_PRINC", "MORTE")) pneu[[length(pneu) + 1]] <- rd |> filter(substr(MUNIC_RES, 1, 2) == "32", # lives in ES substr(DT_INTER, 1, 4) == "2024", # admitted in 2024 substr(DIAG_PRINC, 1, 3) %in% sprintf("J%02d", 12:18)) } bind_rows(pneu) |> summarise(admissions = n(), deaths = sum(MORTE == "1")) #> admissions deaths #> 1 13305 1250 ``` 432 files, no errors, 40.6 minutes. `fetch_datasus()`’s `vars =` helps: only four columns stay in memory. Note `MORTE == "1"`: `fetch_datasus()` returns everything as character, and that is what you compare — after `process_sih()`, the code becomes `"Sim"`/`"Não"`. ### PySUS (Python) ``` python import pandas as pd import pysus ufs = "AC AL AM AP BA CE DF ES GO MA MG MS MT PA PB PE PI PR RJ RN RO RR RS SC SE SP TO".split() comps = [(2024, m) for m in range(1, 13)] + [(2025, m) for m in range(1, 5)] pneumonia = {f"J{n}" for n in range(12, 19)} parts = [] for y, m in comps: for uf in ufs: rd = pysus.ftp.sih(state=uf, year=y, month=m, group="RD", as_dataframe=True) if rd.empty: continue parts.append(rd[rd["MUNIC_RES"].str[:2].eq("32") & rd["DT_INTER"].str[:4].eq("2024") & rd["DIAG_PRINC"].str[:3].isin(pneumonia)]) pneu = pd.concat(parts) len(pneu) #> 9135 ``` ### sih-br-mcp (inside the assistant) The question goes in plain language. The assistant picks the tool, and the call it makes is this: get_hospitalizations { "uf": ["ES"], "year": [2024], "cid_chapter": [10], "group_by": ["cid_group"] } The answer carries one row per ICD group (J12 to J18 add up to 13,305 admissions and 1,250 deaths) and a provenance block saying where each number came from: the billing months read (2024-01 to 2025-04, all 27 file states), the mirror’s vintage, the MD5 of every source `.dbc` and the formal DATASUS citation. First call in the session, 0.9 s; the second, 0.7 s. ## The trap in the middle: PySUS 2.11.7 and the files without a group With `group="RD"`, the loop above returned **9,135** admissions, not 13,305 — 31% fewer, with no error and no warning. PySUS 2.x does not list the DATASUS FTP on each call: it queries its own catalog (a DuckDB database downloaded on first use) pointing to Parquet copies of the files. In that catalog, **4,526 of the 10,946 SIH RD files have no group**. A `group="RD"` query filters by group and does not see them. In this question’s window that is **87 of 432 files** — including 7 of Espírito Santo’s own 16 months (Jan, Feb, Mar, Jul and Nov 2024; Jan and Apr 2025) — and the pneumonia admissions inside them add up to **4,170**. The arithmetic closes: 9,135 + 4,170 = 13,305. Calling **without** `group` reaches those files, together with the month’s other groups (ER, RJ and SP), which you then drop by name. On Windows there is a second defect, upstream of that one: the catalog returns `WindowsPath` objects, their `str()` comes out with backslashes, and a prefix check written with forward slashes discards them all. The SIH calls came back empty, and so did the file listings for SIM and SINASC. That one had already been [reported](https://github.com/AlertaDengue/PySUS/issues/372) and is fixed in the code since 30 October ([PR \#374](https://github.com/AlertaDengue/PySUS/pull/374)), awaiting the next release. The missing-group one I [reported the same day](https://github.com/AlertaDengue/PySUS/issues/380). It is the kind of defect fixed in an afternoon. Meanwhile, anyone using PySUS 2.11 for SIH should check **how many files came back**, not only how many rows. ## Volume, in numbers | | Size | |:---|:---| | Raw SIH-RD microdata, 1992–2026, in the mirror | 23.86 GiB, 11,211 files, 432,173,746 records | | Everything sih-br-mcp’s cube channel publishes | 1.63 GiB | | This post’s question | 1,358 MiB from microdata; 21 KB from sih-br-mcp | | Admissions, days, spending and deaths per year, 1992–2025 | 420,107,114 admissions, one call under 1 s | ## What sih-br-mcp does not do - **Individual records.** The cubes are aggregates; no AIH can be recovered. - **Variables the cubes do not carry**: procedure performed, facility (CNES), admission type, secondary diagnosis. - **Other DATASUS systems**: SIM, SINASC, SIA, SINAN, CNES. - **What DATASUS published today.** The vintage is the mirror’s, and every answer says which. For all of that, use microdatasus or PySUS. ## Which one - **Aggregate question, auditable answer, inside the assistant**: sih-br-mcp. - **Record-level data, variables outside the cubes, other systems, in R**: microdatasus (and [read.dbc](https://cran.r-project.org/package=read.dbc) if you only need to open the `.dbc`). - **The same, in Python**: PySUS, checking how many files came back. - **To check sih-br-mcp**: redo the count from microdata, as this post did. A difference is a bug; [open an issue](https://github.com/SidneyBissoli/sih-br-mcp/issues) with the slice. The three complement more than they compete. sih-br-mcp exists only because the microdata is open and because microdatasus and PySUS showed, for years, what can be done with it. Thanks to Raphael Saldanha and the PySUS team.