Hospital admissions for assault injuries where the perpetrator was recorded as a spouse or domestic partner, a parent or another family member, by financial year from 2020-21, age group and sex, as the number of hospitalisations and the rate per 100,000 people, compiled by the Institute from the National Hospital Morbidity Database. The Institute writes n.p. where a figure is not published.

Where is this table from?

The Institute's Family, domestic and sexual violence data tables workbook, which gathers tables from several national collections. This dataset is one sheet of it. The data in this sheet is compiled by the Institute from a government source it names, and the ABS and AIC publish their own material under CC BY 4.0 as well.

Why does the attribution say "based on" AIHW material?

The Institute publishes its data tables under CC BY 4.0 and asks that material adapted from them, which republishing in other formats is, carry the words "Based on Australian Institute of Health and Welfare material". Every file here carries them. The Institute has not endorsed this site.

Datasets / Commonwealth / AIHW

Family and domestic violence hospitalisations by relationship to perpetrator, Australia

Name: Family and domestic violence hospitalisations by relationship to perpetrator, Australia
Creator: Australian Institute of Health and Welfare
Published: 2026-08-06
License: https://creativecommons.org/licenses/by/4.0/

live

Publisher Australian Institute of Health and WelfareUpdated yearlyLicence CC BY 4.0Latest 2026-08-06Rows 256Fields 6

Table NHMD 1 of the Australian Institute of Health and Welfare's Family, domestic and sexual violence data tables, family and domestic violence hospitalisations by relationship to perpetrator and age group, one row per year, age group, sex, perpetrator relationship and unit, compiled from the National Hospital Morbidity Database.

Also called: FDV hospitalisations, Domestic violence injury hospitalisations, AIHW FDSV data tables NHMD 1, Assault hospitalisations by perpetrator.

Before you use it

What is counted?: Hospital admissions for assault injuries where the perpetrator was recorded as a spouse or domestic partner, a parent or another family member, by financial year from 2020-21, age group and sex, as the number of hospitalisations and the rate per 100,000 people, compiled by the Institute from the National Hospital Morbidity Database. The Institute writes n.p. where a figure is not published.
Where is this table from?: The Institute's Family, domestic and sexual violence data tables workbook, which gathers tables from several national collections. This dataset is one sheet of it. The data in this sheet is compiled by the Institute from a government source it names, and the ABS and AIC publish their own material under CC BY 4.0 as well.
Why does the attribution say "based on" AIHW material?: The Institute publishes its data tables under CC BY 4.0 and asks that material adapted from them, which republishing in other formats is, carry the words "Based on Australian Institute of Health and Welfare material". Every file here carries them. The Institute has not endorsed this site.

Made for agents

Connect your AI agent

Any agent that connects to publicdata.au/mcp can find this dataset, read its 6 fields, count and filter across all 256 rows and diff its versions. Every answer names the version and carries AIHW's attribution. No key.

Then ask it: What does Family and domestic violence hospitalisations by relationship to perpetrator, Australia hold, and what changed in the newest version?

claude mcp add --transport http publicdata https://publicdata.au/mcp

One command. No key, no account, no sign-up.

https://publicdata.au/mcp

Streamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.

Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?

For an assistant that reads the web and has no connector.

ExploreChart every row in your browser

A dashboard over every row. Click to filter, add panels, share a link or embed a frame.

Query APIFilter and count from a URL

aggregate?group=sex&metric=sum.value&unit=eq.Number&year=eq.2024%E2%80%9325&age_group=eq.All%20ages&perpetrator_relationship=eq.Spouse%20or%20domestic%20partner

Hospitalisations for assault by a spouse or domestic partner in the newest year, by sex: 5,176 Female, 663 Male.

Download it as Excel, CSV, JSON and 6 more formats

Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.

Format

Version "Latest" redirects to the newest version and will change when the publisher releases again. A dated version never changes.

https://publicdata.au/d/au-fdv-hospitalisations/latest/data.xlsx

Download

In your own tools

Use it in Excel, R, Python and more

Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.

The dated URL in the code never changes. https://publicdata.au/d/au-fdv-hospitalisations/latest/ redirects to the newest version.

https://publicdata.au/d/au-fdv-hospitalisations/latest/data.csv

In Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.

https://publicdata.au/d/au-fdv-hospitalisations/latest/data.csv

In Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.

library(publicdataau)
df <- pd_read("au-fdv-hospitalisations")
pd_attribution(df)

install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.

import publicdata_au as pd_au
df = pd_au.read("au-fdv-hospitalisations")
df.attrs["publicdata"]["attribution"]

pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.

INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/au-fdv-hospitalisations/v/2026-08-06/data.duckdb' AS au_fdv_hospitalisations (READ_ONLY);
SELECT year, count(*) FROM au_fdv_hospitalisations.records GROUP BY 1 ORDER BY 2 DESC;

The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).

const res = await fetch("https://publicdata.au/api/v1/datasets/au-fdv-hospitalisations/aggregate?group=sex&metric=sum.value&unit=eq.Number&year=eq.2024%E2%80%9325&age_group=eq.All%20ages&perpetrator_relationship=eq.Spouse%20or%20domestic%20partner");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);

The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.

What is in it

Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.

Field	Type	Publisher's header	Note
year	string	Year	The financial year, such as 2024–25.
age_group	string	Age group
sex	string	Sex
perpetrator_relationship	string	Perpetrator's relationship to victim
unit	string	Unit
value	number	Value
suppressed	array		Names of the fields the publisher suppressed in this row. The cells are null.

Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json.

A sample of 10 rows

The hospitalisation counts for all ages in the newest year, for each sex and relationship to the perpetrator. A blank cell is shown as null.

year	age_group	sex	perpetrator_relationship	unit	value
2024–25	All ages	Female	All FDV perpetrators	Number	7287
2024–25	All ages	Male	All FDV perpetrators	Number	2232
2024–25	All ages	Persons	All FDV perpetrators	Number	9528
2024–25	All ages	Female	Other family member	Number	1729
2024–25	All ages	Male	Other family member	Number	1249
2024–25	All ages	Persons	Other family member	Number	2980
2024–25	All ages	Female	Parent	Number	420
2024–25	All ages	Female	Spouse or domestic partner	Number	5176
2024–25	All ages	Male	Parent	Number	328
2024–25	All ages	Male	Spouse or domestic partner	Number	663

The first row as JSON

As it appears in data.json.

GET https://publicdata.au/d/au-fdv-hospitalisations/latest/data.json
{
  "year": "2020–21",
  "age_group": "All ages",
  "sex": "Female",
  "perpetrator_relationship": "All FDV perpetrators",
  "unit": "Number",
  "value": 7069.0,
  "suppressed": []
}

Query it without downloading

The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.

Try rows or aggregate for this dataset. The query builder needs JavaScript.

How filters work

Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.

Operator	What it matches
eq.value	Equal to the value.
neq.value	Not equal to the value. A blank cell does not match.
gt.value	Greater than the value.
gte.value	Greater than or equal to the value.
lt.value	Less than the value.
lte.value	Less than or equal to the value.
like.text	Matches a pattern where * stands for any run of characters. Case-sensitive.
ilike.text	The same as like, ignoring case.
in.(a,b,c)	Equal to any value in the list. A value cannot contain a comma.
is.null	Blank in the source, or suppressed by the publisher.

Parameter	What it does
select	Fields to return, comma-separated. Every field when absent.
order	field.asc or field.desc, comma-separated. The publisher's row order when absent.
limit	Rows per page, 1 to 10,000. 100 when absent.
offset	Rows to skip. The next URL in each answer sets it for you.
group	On aggregate, fields to group by, comma-separated.
metric	On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent.
format	json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers.

Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.

The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.

https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.

What changed

One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.

2026-08-06
256 rows6 fieldsxlsxd822a928d03f

versions.json · changes.json · history.tar.zst (5.2 KB, every version's Parquet and manifest)

Questions

How do I download Family and domestic violence hospitalisations by relationship to perpetrator as a CSV file?

Open https://publicdata.au/d/au-fdv-hospitalisations/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/au-fdv-hospitalisations/v/2026-08-06/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.

Can I open Family and domestic violence hospitalisations by relationship to perpetrator in Excel?

Yes. https://publicdata.au/d/au-fdv-hospitalisations/v/2026-08-06/data.xlsx is a workbook with the 256 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/au-fdv-hospitalisations/v/2026-08-06/data.csv.gz is the CSV at about a tenth of the size.

What years does Family and domestic violence hospitalisations by relationship to perpetrator cover?

The current version covers the years since 2020. Each release from AIHW becomes a new dated version here, and earlier versions stay online.

How often is Family and domestic violence hospitalisations by relationship to perpetrator updated?

AIHW releases it yearly. This site checks the portal every week and adds a dated version when the file changes.

Can I use Family and domestic violence hospitalisations by relationship to perpetrator commercially?

Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.

Is this the official source for Family and domestic violence hospitalisations by relationship to perpetrator?

No. The publisher is Australian Institute of Health and Welfare, and its page is https://www.aihw.gov.au/copyright. This site republishes the publisher's file without changing its content. The original sits beside every version as source.xlsx with its SHA-256, so the two can be compared.

What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.xlsx so the change can be checked.

Attribution and citation

The licence requires the publisher's attribution. We ask that you also name publicdata.au and link to the version you used.

Based on Australian Institute of Health and Welfare material. Australian Institute of Health and Welfare, Australian Government, Family, domestic and sexual violence data tables, National Hospital Morbidity Database table NHMD 1, sourced 30 September 2026, https://www.aihw.gov.au/family-domestic-and-sexual-violence/resources/data-downloads, licensed under CC BY 4.0. Serialised and versioned by National Digital at publicdata.au, https://publicdata.au/d/au-fdv-hospitalisations/v/2026-08-06/

The attribution alone is inside every file. Harvard is the author-date form a report or a brief asks for. Paste the HTML on a web page so the links carry.

Licence record

Australian Institute of Health and Welfare states the licence, CC BY 4.0, on its own page, which was read and recorded here on 1 October 2026.

Every fetch reads the licence again and stops when it has changed. The fetch that made version 2026-08-06 read it on 30 September 2026.

Also published on

Hugging Faceload_dataset("National-Digital/au-fdv-hospitalisations")
Kagglewith a starter notebook
ZenodoDOI 10.5281/zenodo.23077600

Each copy carries the same attribution and links back to this page.

For agents and tools

datapackage.jsonFrictionless data package pointing at the latest version
schema.jsonTable Schema for every field
schema.xlsxthe same field list as an Excel data dictionary
versions.jsonevery version with date, rows, fields and hash
changes.jsondiff for every consecutive pair
manifest.jsonwhere the bytes came from and when
Query APIfiltered rows and counts without a download
openapi.jsonOpenAPI 3.1 for this dataset's query paths
explore/charts and dashboards in the browser, shareable and embeddable
index.mdthis page as Markdown

This page carries a schema.org Dataset record and is listed in /.well-known/ard.json and /catalog.json.

Source

Australian Institute of Health and Welfare publishes this table on www.aihw.gov.au. Questions about the content go to the publisher. Questions about the files here go to corrections.

Fetched 30 September 2026. Source SHA-256 d822a928d03fccd6…

Improve this dataset

This page is built from register/au-fdv-hospitalisations.yaml on GitHub. Anyone can propose a fix to it, such as a clearer description or a field the publisher renamed, as a pull request.

Report a file that differs from the publisher's · How to contribute