Advocacy Atlas

31,871 individual submissions have reached the committee that decides which medicines Australia pays for.

Most Public Summary Documents since 2014 carry a section recording what patients, carers, clinicians and organisations told the PBAC. It is the only non-technical layer in the entire reimbursement record, and until now it was organised nowhere — not by who spoke, and not by what they spoke about. This is that register, built from 3,056 decisions since 2005.

31,871 individuals
3,473 health professionals
2,062 organisation submissions
91 named organisations

Counted in the documents' own words. 1,161 decisions record consumer input; 579 state plainly that none was received. These are submissions rather than unique people — one campaign meeting two submissions at the same meeting is recorded against both.

The record got louder

The consumer comments facility barely registered before 2013. By 2024, four in five decisions carried something from a patient, a carer, a clinician or an organisation.

Decisions carrying consumer inputshare of decisions per year
no comments recorded 0% 25% 50% 75% 100% 78% '11'13'15'17'19'21'23'25
Individuals per decision that carried inputsame timeline, different measure
no comments recorded 0 30 60 90 Orkambi '11'13'15'17'19'21'23'25

Participation broadened and thinned at the same time. Far more decisions now attract comment, but the typical number of people behind each one has fallen from about 30 to about 20 — the 2018 peak is a single campaign, not a trend.

View as a table
YearDecisionsWith inputShareIndividualsPer decision
2011870 0%00
20121310 0%00
2013985 5%6112.2
201414951 34%1,31425.8
201519472 37%2,18330.3
201617593 53%2,61228.1
201717481 47%2,91135.9
201818794 50%10,570112.4
201919891 46%2,75730.3
202016296 59%1,47115.3
2021161109 68%1,0329.5
2022156106 68%1,49014.1
2023144112 78%9448.4
2024156126 81%1,79214.2
2025156121 78%2,61521.6

When a lot of people write at once

The largest write-ins in the record are overwhelmingly rare disease, and mostly in children. Outcomes are mixed — a large campaign is not a decision.

MedicineIndividualsYearOutcome
lumacaftor with ivacaftorTreatment of cystic fibrosis in patients aged 6–11 years who are homozygous for the… 3,980 2018 Recommended with restriction
lumacaftor with ivacaftorTreatment of cystic fibrosis in patients aged ≥12 years who are homozygous for the… 3,980 2018 Not recommended
nusinersenTreatment of infantile-onset (Type I) and childhood-onset (Types II and III) spinal… 1,087 2017 Not recommended
sapropterinTreatment of hyperphenylalaninaemia (HPA) caused by phenylketonuria (PKU) in… 919 2018 Deferred
lumacaftor with ivacaftorTreatment of cystic fibrosis in patients aged 12 years and older who are homozygous for… 594 2016 Not recommended
belzutifanTreatment of adult patients with von Hippel-Lindau (VHL) disease who require therapy for… 516 2024 Recommended with restriction
lumacaftor with ivacaftorTreatment of cystic fibrosis in patients aged 12 years and older who are homozygous for… 507 2016 Not recommended
eflornithinePost-maintenance treatment to prevent relapse in patients with high-risk neuroblastoma… 485 2025 Recommended with restriction

Lumacaftor with ivacaftor appears twice because one campaign met two submissions at the same meeting — one for children aged 6–11, recommended, one for everyone 12 and over, not recommended. The same 3,980 people are recorded against both.

Being heard is not evenly distributed

Consumer input reaches roughly three-quarters of cancer and rare-disease decisions and about one in seven metabolic ones. Restricted to 2014 onward, where the section is reliably present in the document.

Metabolic 14% 161
Pain 30% 43
Infectious disease 33% 52
Ophthalmology 40% 65
Rheumatology 42% 112
Mental health 44% 27
Endocrinology 45% 150
Women's health 53% 47
Vaccines 55% 62
Cardiovascular 58% 77
Dermatology 60% 85
Neurology 61% 157
Immunology 61% 28
Respiratory 62% 99
Haematology 65% 187
Hepatology 65% 49
Oncology 73% 483
Gastroenterology 76% 58
Rare disease 78% 89

The era cut matters. On the full corpus, mental health appears to sit at 19% and cardiovascular at 28% — both artefacts of having more pre-2014 decisions, where the section often does not exist. In the modern era they are 44% and 58%. Metabolic is the one that holds.

The conditions nobody spoke for

Grouped by disease rather than by drug, using the classified indication for each submission, and cut at 2014 for the same reason as the chart above. The pattern the therapy-area view hides: it is not metabolic medicine broadly, it is the inherited metabolic disorders of childhood. All 882 classified conditions are browsable in the disease index, each with every medicine considered for it.

ConditionDecisionsWith inputShare
dry eye syndromeOphthalmology 11 0
tyrosinaemiaMetabolic 10 0
fat malabsorptionMetabolic 8 0
maple syrup urine diseaseMetabolic 6 0
inborn errors of protein metabolismMetabolic 5 0
chronic severe painPain 5 0

The voices heard most often

OrganisationAppearancesDrugsYearsSpeaks as
Rare Cancers Australia 121 74 2014–2026 patient organisation
Medical Oncology Group Australia 109 52 2014–2025 professional body
Lung Foundation Australia 66 50 2015–2025 patient organisation
Leukaemia Foundation 47 29 2019–2025 patient organisation
Colitis Australia 31 14 2017–2025 patient organisation
Breast Cancer Network Australia 27 15 2016–2025 patient organisation

The second most frequent voice for patients in the Australian record is a clinicians' body, not a patient one.

The full atlas

Free

Free account required

Free — your email gets a magic link. Unlocks the full Field Matrix (all columns, export), Studio workspace, grounded Ask answers, and the Cycle Briefing.

No password. Check your inbox (and spam), then click the link to return with access.

How this was built, and what it cannot tell you

The measurement is read directly from each document, with no model involved. The PBAC states all three signals plainly: whether comments were received, how many individuals, health professionals and organisations wrote, and which organisations they were. A model would add nothing there except a way to be wrong.

The grouping is a different matter and worth separating. Therapy area and the condition each submission was for are classified by a language model from the indication text, because the documents do not carry either as a field. So the counts on this page are the document's own; the buckets they are sorted into are an interpretation, and an individual condition label can be wrong. That is why the conditions are browsable one by one rather than only in aggregate.

Three limits worth stating. 35% of decisions have no recoverable consumer-comments section, overwhelmingly before 2014, which is why both the therapy-area and condition views are cut at that year. Read across the whole corpus instead, the quietest conditions come back as hypertension, postmenopausal osteoporosis and major depression, none of which are silent: their decisions simply pre-date the facility. And silence in this register means no comment reached the committee through the consumer facility — not that no one cared, and not that nobody was affected.

People are counted here, never quoted. Organisations are public actors making public submissions and are named; individual accounts of illness stay in the documents they were written for.