A population-based model of indicators of allied health workforce needs: proof-of-concept in podiatry
Joshua Horsley A * and Susan Nancarrow A B C *A
B
C
Abstract
Allied health workforce planning faces challenges because of insufficient metrics that accurately reflect population need for services. This paper presents a method and proof-of-concept in the podiatry profession for developing population-based need indicators and indices suitable for allied health workforce planning and comparative benchmarking.
Population-based indicators of podiatry need were selected and combined into an index of need by Statistical Area Level 3 (SA3) in Australia. Medicare patient age and sex distributions for relevant item numbers were used to determine the inclusion of specific population age groups and sex as indicators. Other indicators included diabetes, socioeconomic status, and Aboriginal and Torres Strait Islander status. The need index was calculated based by aggregating these indicators at the SA3 level. The resulting need index was compared with podiatry supply (per capita clinician counts) using a population-weighted correlation coefficient (pwCorr).
Analysis of Medicare usage data led to the inclusion of indicators: population aged 65 and over, and female gender. The need index had a small but significant negative correlation with supply at the SA3-level (pwCorr = −0.12, P = 0.03) and positively, but not significantly, correlated at the state/territory-level (pwCorr = 0.42, P = 0.30).
Developing profession-specific population-based need indices provides a valuable tool for allied health workforce planners to benchmark need and supply within professions. Combining single need indicators with supply metrics offers a concise framework for effective workforce planning and advocacy.
Keywords: allied health, need index, need indicator, podiatry, population health, population ratio, workforce planning, workforce ratio, workforce supply.
References
1 Gillam M, Leach M, Muller J, Gonzalez-Chica D, Jones M, Muyambi K, et al. Availability and quality of publicly available health workforce data sources in Australia: A scoping review protocol. BMJ Open 2020; 10(1): e034400.
| Crossref | Google Scholar | PubMed |
2 Sutton C, Prowse J, McVey L, Elshehaly M, Neagu D, Montague J, et al. Strategic workforce planning in health and social care – an international perspective: A scoping review. Health Policy 2023; 132(October 2022)): 104827.
| Crossref | Google Scholar | PubMed |
3 Kroezen M, Van Hoegaerden M, Batenburg R. The Joint Action on Health Workforce Planning and Forecasting: Results of a European programme to improve health workforce policies. Health Policy 2018; 122(2): 87-93.
| Crossref | Google Scholar | PubMed |
4 Santana IR, Mason A, Gutacker N, Kasteridis P, Santos R, Rice N. Need, demand, supply in health care: Working definitions, and their implications for defining access. Health Econ Policy Law 2021; 18: 1-13.
| Crossref | Google Scholar | PubMed |
5 Hearn C, Ross JA, Govier A, Semciw AI. Clinical care ratios for allied health practitioners: an update and implications for workforce planning. Aust Health Rev 2024; 48: 562-8.
| Crossref | Google Scholar | PubMed |
6 Zhang X, Lin D, Pforsich H, Lin VW. Physician workforce in the United States of America: Forecasting nationwide shortages. Hum Resour Health 2020; 18(1): 8.
| Crossref | Google Scholar | PubMed |
7 Tait D, Davis D, Roche MA, Paterson C. Nurse/midwife-to-patient ratios: A scoping review. Contemp Nurse 2024; 60(3): 257-69.
| Crossref | Google Scholar | PubMed |
8 Nancarrow SA, Young G, O’Callaghan K, Jenkins M, Philip K, Barlow K. Shape of allied health: An environmental scan of 27 allied health professions in Victoria. Aust Health Rev 2017; 41(3): 327-35.
| Crossref | Google Scholar | PubMed |
9 R Core Team. R: A Language and Environment for Statistical Computing. 2023. Available at https://www.r-project.org/
10 Australian Bureau of Statistics. Australian Statistical Geography Standard (ASGS) Edition 3. Canberra; 2021. Available at https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/latest-release [verified 1 July 2024].
11 Australian Institute of Health and Welfare. Medicare-subsidised GP, allied health and specialist health care across local areas: 2021–22. 2022. Available at https://www.aihw.gov.au/reports/primary-health-care/medicare-subsidised-gp-allied-health-and-specialis
12 Services Australia. Medicare Item Reports. 2024. Available at http://medicarestatistics.humanservices.gov.au/statistics [verified 1 May 2024].
13 Department of Health and Aged Care. Medicare Statistics: Explanatory Notes. 2024. Available at https://www.health.gov.au/sites/default/files/2024-02/explanatory-notes-for-medicare-statistics.pdf
14 Australian Bureau of Statistics. Community Profiles. 2022. Available at https://www.abs.gov.au/census/guide-census-data/about-census-tools/community-profiles [verified 1 July 2024].
15 Australian Bureau of Statistics. Socio-Economic Indexes for Areas (SEIFA), Australia. 2021. Available at https://www.abs.gov.au/statistics/people/people-and-communities/socio-economic-indexes-areas-seifa-australia/2021 [verified 1 July 2024].
16 Australian Bureau of Statistics. National Health Survey: Small Are Estimates, 2017–18 — Australia. 2020. Available at https://www.abs.gov.au/statistics/health/health-conditions-and-risks/national-health-survey/2017-18 [verified 1 July 2024].
17 Department of Health and Aged Care. National Health Workforce Dataset. 2024. Available at https://hwd.health.gov.au/resources/information/nhwds.html [verified 1 May 2024].
18 Australian Bureau of Statistics. ERP by SA2 (ASGS 2021), Age and Sex, 2001 Onwards. [Data Explorer] 2024. Available at https://explore.data.abs.gov.au/vis?lc=en&fs%5B0%5D=People%2C0%7CPopulation%23POPULATION%23&pg=0&fc=People&df%5Bds%5D=PEOPLE_TOPICS&df%5Bid%5D=ABS_ANNUAL_ERP_ASGS2021&df%5Bag%5D=ABS&df%5Bvs%5D=1.2.0&pd=%2C&dq=...A&ly%5Bcl%5D=TIME_PERIOD&ly%5Brw%5D=ASGS_2021&ly%5Brs%5D=REGION_TYPE [verified 29 Nov 2024].
19 Paisey RB, Abbott A, Levenson R, Harrington A, Browne D, Moore J, et al. Diabetes-related major lower limb amputation incidence is strongly related to diabetic foot service provision and improves with enhancement of services: peer review of the South-West of England. Diabet Med 2018; 35(1): 53-62.
| Crossref | Google Scholar | PubMed |
20 Australian Institute of Health and Welfare. Health across socioeconomic groups. 2022. Available at https://www.aihw.gov.au/reports/australias-health/health-across-socioeconomic-groups [verified 1 May 2024].
21 Australian Institute of Health and Welfare. Australian Burden of Disease Study: impact and causes of illness and death in Aboriginal and Torres Strait Islander people 2018. 2022. Available at https://www.aihw.gov.au/reports/burden-of-disease/illness-death-indigenous-2018
22 West M, Chuter V, Munteanu S, Hawke F. Defining the gap: A systematic review of the difference in rates of diabetes-related foot complications in Aboriginal and Torres Strait Islander Australians and non-Indigenous Australians. J Foot Ankle Res 2017; 10(1): 48.
| Crossref | Google Scholar | PubMed |
23 Department of Health and Aged Care. Medicare Benefits Schedule. 2024. Available at https://www9.health.gov.au/mbs [verified 30 August 2024].
24 McNicholl SG, Reid D, Bright F. Clinical Care Ratios: Differences in Allied Health roles in New Zealand. Aust Health Rev 2024; 48: 556-61.
| Crossref | Google Scholar | PubMed |
25 Department of Health and Aged Care. Health Demand and Supply Utilisation Patterns Planning (HeaDS UPP). 2021. Available at https://hwd.health.gov.au/headsupp [verified 1 May 2024].
