From population numbers to population needs: Incorporating epidemiological change into health service planning in Australia
Bibliographic record
Abstract
BACKGROUND: In the face of rapidly ageing populations and increasing costs of health care provision, questions continue to be raised about the long-term sustainability of publicly funded health care programmes around the world. But despite increasing evidence of dynamic changes in epidemiology, most official health service planning models continue to rely on the implicit assumption that age-specific requirements for services (and by implication age-specific needs for care) will remain constant across future years ('constant-use models'). OBJECTIVES: In this paper, we discuss the advantage of dynamic 'changing needs' planning models, compared to 'constant-use' planning models, and consider a framework that integrates population needs directly into health service planning. Using Australian survey data, we empirically illustrate the difference between static health service planning approaches to dynamic needs-driven planning models. METHODS: We use data from the Household, Income and Labour Dynamics Survey in Australia (HILDA) to explore trends in health needs from 2001 to 2020. We subsequently simulate a 'changing-needs' planning model where changes in health needs by birth-cohorts are incorporated into official government estimates from the Australian Intergenerational Reports (IGR) to understand the potential impact on future health care requirements. RESULTS: Our results show that healthy ageing trends are being observed for successive birth-cohorts with these trends greatest in older age groups, the age groups for which health care expenditures are largest. Adjusting for these changes in needs using Australian data leads to reductions in the expenditures required for future years ranging from 1.5 (2.50%) to 3 billion (5.25%) 2019 AUD. CONCLUSION: We conclude that 'constant-use' planning models based on the expected future numbers of people in different age groups applied to current levels of service use by age groups without any consideration given to changing age-specific needs for health care lead to inefficient resource planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".