HRF Report V6 (jpeg v).indd · Web viewHigh risk foot admissions comprise five different diag- nostic and procedural sub-groups; cellulitis of the lower limb, peripheral vascular

High Risk Foot: Geographical Inequi- ties, Importance of Different Diagnosis Groups, Forecast Hospitalisations, and Access to ServicesFirst Edition: Fourth Iteration December 2017

CHASM 3

Contributors

Matthew Tuson2

Mei Ruu Kok1

Matthew Yap1 Berwin Turlach2 Alistair Vickery1 David Whyatt1

1. School of Medicine; Faculty of Health and Medical Sciences; University of Western Australia. M706, 35 Stirling Highway, Crawley, WA 6009, Australia.

2. School of Mathematics and Statistics, University of Western Australia. M019, 35 Stirling Highway, Crawley, WA 6009, Australia.

*Corresponding author: Dr David WhyattSchool of MedicineFaculty of Health and Medical Sciences The University of Western Australia M706, 35 Stirling Highway CrawleyWA 6009T +61 8 6457 7326F +61 8 6457 3120E [email protected]

Acknowledgements

The Department of Health, Western Australia, Data Link- age BranchThe WA Hospital Morbidity Data Collection Dr Joanna ScheepersAll individuals and organisations providing data for the updated podiatry services FTE map

Ethical review

The project was approved by WA Department of Health Human Research Ethics Committee (DOH HREC) and the WA Aboriginal Health Ethics Committee (WAAHEC).

Funding

This work was funded by the WA Department of Health.

Citation

This report may be cited as:

High Risk Foot: Geographical Inequities, Importance of Different Diagnosis Groups, Forecast Hospitalisations, and Access to Services. Collaborative for Healthcare Anal- ysis and Statistical Modelling (CHASM), December 2017.

Document control

This report is in its fourth iteration. Changes made between the third and fourth iterations include the following:

1. Re-formatted design for public dissemination.2. Typographical errors addressed throughout.

Changes made between the second and third iterations include the following:

1. Revised Key Findings and Recommendations to improve interpretability and accuracy.

2. Updated Table 2 to include counts of individuals and hospital costs for existing variables and financial year.

3. Corrected counts of individuals with HRF admissions.

4. Typographical errors addressed throughout.

Changes made between the first and second iterationsinclude the following:

1. Changes in the diagnosis/procedure codes used to identify high risk foot admissions (refer to Table 1):

a. Some admissions for ‘Osteomyelitis (of the foot/ ankle)’ are identified using the codes M86.57 and M86.67 instead of M83.57 and M83.67.

b. Admissions for ‘Charcot’s Foot’ are identified using the dagger/asterisk combinations G98+/M14.6* or A52.1+/M14.6*.

c. Admissions for ‘Cellulitis of Lower Limb’ now include admissions with diagnosis L03.02 ‘Cellulitis of the Toe’ and also L03.13 and L03.14.

2. Inclusion of an updated 2017 map of podiatry services FTE (the previous iteration included a map based on 2010 data).

3. A revised structure to aid interpretability.4. Updated analyses and results based on a more

recent data extract of the WA Data Collections, made available to CHASM since the previous iteration.

5. Minor grammatical changes throughout.

Note regarding use of the term ‘Aboriginal’

In this document, the term ‘Aboriginal’ is used to refer to both Aboriginal and Torres Strait Islander Peoples.

mailto:[email protected]

4 The University of Western Australia

Table of Contents

Contributors 3

Acknowledgements 3Ethical review 3Funding 3Citation 3Document control 3Note regarding use of the term ‘Aboriginal’ 3

List of Figures 5

List of Tables 6

List of Acronyms 6

Summary 7

Key findings 8

1. The burden of High Risk Foot (HRF) admissions8

2. Demographic distribution of HRF admissions 83. Geospatial analysis of HRF 84. Sustained ratchet in HRF hospital costs 85. Predicting future areas of high demand for

HRF services8

Recommendations 9

Background 10

Additional data required 10

Section 1. Describing high risk foot admissions 11

Summary of Section 1 11Identifying high risk foot admissions 12Sociodemographic distribution of high risk foot admissions 13Geographic distribution of high risk foot admissions 14

Section 2. Describing high risk foot sub- groups 15

Summary of Section 2 15High risk foot sub-groups 16Temporal distribution of high risk foot admissions by diagnosis sub-group 16Sociodemographic distribution of high risk foot admis-

sions by diagnosis sub-group 17Comorbidity of high risk foot diagnoses 18Geographic distribution high risk foot admissions by diagnosis sub-group 18

Section 3. Longitudinal hospital demand associated with cardinal high risk foot admissions 24

Summary of Section 3 24Defining cardinal high risk foot admissions 25Hospital beds occupied by individuals who have had a cardinal high risk foot admission in the past 25Estimating longitudinal hospital demand of patients with cardinal high risk foot admissions 25Estimating hospital costs 25Mean longitudinal hospital costs among patients with cardinal high risk foot admissions 26Mean longitudinal inpatient days among patients with cardinal high risk foot admissions 26Mean longitudinal hospital demand among diagnosis sub-groups of cardinal high risk foot admissions 29

Section 4. Forecasting areas with high rates of high risk foot admissions 33

Summary of Section 4 33Describing geographical inequity 34Identifying hotspots of high risk foot admissions 34Candidate predictors 34Metropolitan and state-wide models 34Results 35Important predictors 35Performance of optimal models 36Metropolitan forecasts 37Regional forecasts 38

Section 5. Identifying areas for targeted intervention 42

Summary of Section 5 42Potential cost savings arising from prevention of cardinal high risk foot admissions 43Accessibility to podiatry health services 43Discussion 48Caveats 48

References 49

CHASM 5

List of Figures

Figure 1. Mean annual number of high risk foot admissions between July 1st 2011 and June 30th 2016, by SA2 region. 14Figure 2. Counts of admissions over time by sub-group of high risk foot 16Figure 3. Age distribution of patients by HRF sub-group.

18Figure 4 Mean annual number of amputation admissions between 2011/12 and 2015/16, by SA2 region. 19Figure 5 Mean annual number of cellulitis admissions between 2011/12 and 2015/16, by SA2 region. 20Figure 6 Mean annual number of diabetic foot admissions between 2011/12 and 2015/16, by SA2 region. 21 Figure 7 Mean annual number of PVD admissions between 2011/12 and 2015/16, by SA2 region. 22Figure 8 Mean annual number of osteomyelitis/Charcot’s foot admissions between 2011/12 and 2015/16, by SA2 region. 23Figure 9. Cumulative hospital beds occupied (per day) by individuals with a previous cardinal high risk foot admission. 25Figure 10. Mean longitudinal hospital costs (measured as inpatient bed day costs plus ED presentation costs) associated with patients with cardinal high risk foot admissions 27Figure 11. Mean longitudinal hospital costs (inpatient bed day costs plus ED presentation costs, per person-year) associated with patients with cardinal high risk foot admissions. Data is shown separated by age-group (<55, 55-64, 65-74, 75-84, 85+) 27Figure 12. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions 28Figure 13. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions. Data is shown separated by age group (<55, 55-64, 65-74, 75-84,85+). 28Figure 14. Mean longitudinal inpatient demand (measured as bed days occupied) among patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot, and diabetic foot 31Figure 15. Mean longitudinal hospital costs (measured as inpatient bed day costs plus ED presentation costs) associated with patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/ Charcot’s foot, and diabetic foot 31Figure 16. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot, and diabetic foot 32Figure 17. Mean longitudinal hospital costs (inpatient bed day costs plus ED presentation costs, per person-year) associated with patients with cardinal high risk foot admissions for cellulitis, PVD, amputation,

osteomyelitis/


Charcot’s foot, and diabetic foot 32Figure 18 Ratio ASSRs of HRF admissions (vs metropolitan mean rate) among SA2s (left to right) 3-year, 4-year, and 5- year metropolitan models. 37Figure 19 A Ratio ASSRs of HRF admissions (vs state mean rate) among SA2s (left to right) 3-year, 4-year, and 5- year state models. 37Figure 20 Areas forecast by at least one of the optimal metropolitan models. 39Figure 21 Areas forecast least one of the optimal state- wide models. 41Figure 22 Number of cardinal high risk foot admissions per year in the 27 forecast potential priority areas. 43Figure 23 Location and FTE of podiatry clinical services in metropolitan region, 2017 44Figure 24 Mean annual population of individuals admitted with high risk foot diagnoses between 2011/12 and 2015/16, by SA2 region 45Figure 25 Mean population-weighted percentile index of accessibility to podiatry services between 2011/12 and 2015/16, by SA2 region. 46Figure 26 Mean percentile index of accessibility to podiatry services relative to the mean annual population of residents admitted with high risk foot conditions. Each point represents an SA2 region in metropolitan Perth. 47

CHASM 7

List of Tables

Table 1 Diagnosis/procedure groups comprising high risk foot admissions 12Table 2. Distribution of HRF admissions occurring between July 1st 2011 and June 30th 2016, among WA residents. 13Table 3. Percentage distribution of HRF admissions among WA residents, by sociodemographic and admission sub-groups. Data is for admissions occurring between July 1st 2011 and June 30th 2016. 17Table 4 Most common high risk foot sub-group combinations among unique individuals. 18Table 5 Mean number of hospital bed days occupied by patients with a cardinal high risk foot admission, in time periods relative to the date of the cardinal admission 30 Table 6 Mean hospital cost ($K AU 2014) per patient with a cardinal high risk foot admission, in time periods relative to the date of the cardinal admission 30Table 7 Predictors in the optimal metropolitan models.

35Table 8 Predictors in the optimal state-wide models. 35 Table 9 Pooled validation statistics for the optimal 3-year (2016-19), 4-year (2016-20), and 5-year (2016-21) metropolitan and state-wide prediction models. 37Table 10 Areas forecast by the optimal metropolitan models 38Table 11 Areas forecast by the optimal state-wide models. 40

List of Acronyms

ABS Australian Bureau of StatisticsAIHW Australian Institute for Health and Welfare ASSR Age-sex standardised rateCHASM Collaborative for Healthcare Analysis

and Statistical ModellingDOH HREC WA Department of Health Human

Research Ethics CommitteeED Emergency DepartmentGP General PracticeHRF High risk footICD-10-AM International Statistical Classification of Dis

eases and Related Health Problems, Tenth Revision, Australian Modification

IEO Index of education and occupationIER Index of economic resourcesIRSAD Index of relative socio-economic

advantage and disadvantageIRSD Index of relative socio-economic

disadvan tageLCL Lower confidence limitLHS Learning health systemMH Mental healthNMHS North-Metropolitan Health Service OM/C Osteomyelitis and Charcot’s FootPPV Positive predicted valuePVD Peripheral Vascular DiseaseRR Rate ratioSA2 Statistical area level 2 boundarySA4 Statistical area level 4 boundarySEIFA Socio-economic indices for areasUCL Upper confidence limitWA Western AustraliaWAAHEC WA Aboriginal Health Ethics Committee


SummaryThe research group CHASM (Collaborative for Healthcare Analysis and Statistical Modelling) was commissioned by the Office of the Chief Professions Health Officer to provide high level analysis and statistical and geospatial modelling to inform clinical service planning and service evaluation for podiatry services for High Risk Foot (HRF). This report provides detailed analysis and accurate predictions that have not been previously possible without generous access to WA health data and the new statistical analysis and computing power at CHASM.

High risk foot admissions comprise five different diagnostic and procedural sub-groups; cellulitis of the lower limb, peripheral vascular disease (PVD), amputations of the lower limb, diabetic foot ulcer, and osteomyelitis/ Charcot’s foot. The differential geographical distribution of high risk foot admissions is examined, along with cost and utilisation of hospital resources by those with high risk foot admissions.

Section 1 of this document describes the diagnosis and procedure codes used to identify high risk foot admissions. The geographical distribution of such admissions in described.

Section 2 describes the socio-demographic and geographic distribution of subsets of high risk foot types of admissions.

Section 3 examines the longitudinal demand associated with high risk foot admissions, including the total number of beds occupied by patients who have such an admission in the past; and hospital costs in the years pre- ceding and following such an admission.

Section 4 describes forecasts of areas with high rates of high risk foot admission for 3-5 years into the future (i.e. up until the 2020/21 financial year).

Section 5 examines the potential impact of targeted interventions and identifies potential priority areas within areas forecast to have high rates of high risk foot admission into the future.

CHASM 9

Key findings

1. The burden of High Risk Foot (HRF) admissions

HRF admissions lead to a substantial and sustained cost to the health and wellbeing of the WA population and are a significant economic burden to WA health. The total hospital cost of HRF admissions was approximately $150 million per year in 2015/16. There were 149,639 HRF admissions for 69,020 unique individuals between January 1st 2002 and June 30th 2016. The number of individuals with a HRF admission increased by 60% from 5,269 in 2002/03, to 8,377 individuals in 2015/16. On any one day, approximately 9% of WA hospital beds are occupied by individuals who have had a cardinal HRF admission in the last 10 years (this calculation excluded inpatient days associated with dialysis).

2. Demographic distribution of HRF admissions

Diagnosis and procedure groups which are collective- ly referred to as HRF include cellulitis of the lower limb, peripheral vascular disease (PVD), lower limb amputations, diabetic foot ulcer, and osteomyelitis/Charcot’s foot. The number of admissions within the HRF sub- groups varied according to demographic status. For example, admissions for PVD occurred primarily in older patients and among males, with approximately 80% of PVD admissions occurring in patients aged 65 or above and 66% among males. For cellulitis, 35% of admissions were for those under 55 years of age. Diabetic foot ulcer admissions were five times more common in Aborigi- nal patients who represented 17% of such admissions despite representing only 3% of the population. For PVD, only 3% of admissions were in Aboriginal patients.

3. Geospatial analysis of HRF

Relatively high numbers of all types of HRF admissions were found in the metropolitan areas around Fremantle, Rockingham, and in large parts of northern and south- east Perth, including Armadale, Bentley, Midland, Balga, and Mirrabooka. The Kimberley area had high numbers of all HRF admission types, except PVD.

4. Sustained ratchet in HRF hospital costs

Sustained and dramatic increases (a ratchet effect) in mean hospital costs and mean inpatient days often oc- cur following a cardinal HRF admission (note: cardinal admissions are defined as the first admission with a high risk foot diagnosis occurring within 3 years). This ratchet in costs was particularly pronounced in patients aged 55- 64, where mean annual hospital costs rose from approxi-

mately $2,000 to $7,000 and the number of inpatient days per person-year increased from approximately three to seven days following a cardinal admission. This change was sustained for at least ten years. Similar results were observed within sub-group HRF admissions. The ratchet increase in inpatient demand was less pronounced for cellulitis and PVD compared to the other diagnoses. By comparing costs before and after a cardinal admission, it could be speculated that cost savings of up to $40,000 over six years might be attained by preventing cardinal high risk foot admissions within the relatively high cost types of amputation, osteomyelitis/Charcot’s foot, or diabetic foot.

5. Predicting future areas of high demand for HRF services

Accurate prediction models are able to identify specific metropolitan and regional areas that will have high rates of HRF admission up to five years into the future (e.g. a positive predictive value of 75% (95%CI:60-89) and sensitivity of 60% (95%CI 46-80) in state-wide five-year forecasts). It is important to balance the sensitivity and PPV achieved by prediction models when planning health interventions.

Among 14 metropolitan SA2s forecast to have a disproportionately high rate of HRF admissions up to five years into the future, Balga – Mirrabooka is identified as a potential priority area for a place-based podiatry services intervention: it had a relatively poor index of accessibility to podiatry services combined with a relatively high population of individuals admitted with high risk foot diagnoses.


Recommendations1. Areas forecast to have a disproportionately high

burden of high risk foot admissions in the future may be considered potential priority areas for place-based interventions. However, the allocation of resources towards interventions should take into account the mix of different high risk foot diagnosis/procedure groups in targeted areas, since different conditions are associated with different longitudinal patterns of hospital use. The following metropolitan and regional areas are identified as potential priority areas for intervention:

Regional areas:Broome Kununurra Geraldton - East

Carnarvon Narrogin RoebuckDerby - West

Gnowangerup South Hedland Kimberley

Geraldton Kalgoorlie Halls Creek York - Beverley

Metropolitan areas:Armadale - Wun-

gong - Brook- dale

Girrawheen Rivervale - Kew- dale - Cloverdale

Balga - Mirra- booka Maylands Willagee

Bentley - Wilson- St James

Midland - Guild- ford Lockridge - Kiara

Nollamara - Westminster

Belmont - Ascot-Redcliffe

Cannington - Queens Park Parmelia - Orelia

2. Balga-Mirrabooka is identified as a priority area for a place-based podiatry services intervention. This is based on the combination of being an area predicted to have high rates of HRF admissions until 2021, relatively poor access to podiatry services, and a large population of individuals being admitted for high risk foot.

Calista

BackgroundThis is a foundation document for the establishment of a Learning Health System (LHS) in context of delivery of podiatry services across Western Australia.

The establishment of LHS for podiatry services is planned across three stages:

1. To create an ongoing methodology to optimise access to podiatry services.

2. To examine the relationship of podiatry services and patient outcomes at a locally-aggregated level.

3. To examine the impact and return on investment from podiatry services upon individuals via patient-linked data across various datasets.

To address these aims, the following topics are examined in this report:

- A description of diagnosis/procedure sub-groups comprising high risk foot admissions,

- The characteristics of individuals admitted with high risk foot diagnoses,

- Longitudinal measurement of hospital inpatient demand and costs associated with individuals admitted with high risk foot diagnoses, and

- Forecasting geographical areas expected to have above average rates of high risk foot admissions up to five years into the future.

The differential geographical distribution of high risk foot admissions by sub-group are examined, along with the differential utilisation of hospital resources by individuals admitted with high risk foot types.

Furthermore, this report is intended to support future col- laborations in the ongoing improvement of podiatry service delivery in Western Australia, with the ultimate goal of reducing the burden of foot-related conditions within communities. The approaches used here represent a starting point of communication that may be improved by clinical input and by taking into account other practi- cal considerations related to the analyses. This is an iter- ative process that will evolve as new questions, new data, and new knowledge are incorporated.

Additional data required

To determine the impact of podiatry service delivery on patient outcomes and state-wide hospital costs, historical occasion-of-service podiatry data from clinics is additionally required. Results from counterfactual analyses of such data, when linked to hospital records from the WA Data Collections, would support the allocation of podiatry resources to populations most in need.

Section 1. Describing high risk foot admissions

Summary of Section 1

High risk foot admissions comprise five different sub-groups, namely:

i) Cellulitis of the lower limb,ii) Peripheral vascular disease (PVD),iii) Amputations of the lower limb,iv) Diabetic foot ulcer, andv) Osteomyelitis/Charcot’s foot

There were 149,639 high risk foot admissions among 69,020 individuals between 1st January 2002 and 30th June 2016. The number of individuals with HRF admissions increased from 5,269 in 2002/03 to 8,377 in 2015/16. The total hospital cost of HRF admissions was approximately $150 million per year in 2015/16.

The burden of high risk foot admissions varied by age, gender and Aboriginal status. The geographical distribution of high risk foot admissions from 1st July 2011 to 30th June 2016 is also presented.

Identifying high risk foot admissions

Diagnosis (principal and additional) and procedure codes were used to identify high risk foot (HRF) admissions and their sub-groups. Sub-groups were: i) cellulitis of the lower limb; ii) peripheral vascular disease (PVD);iii) amputations (over the lower limb; above knee, below knee, or of the toe); iv) diabetic foot; and v) osteomyelitis (of the foot/ankle)/Charcot’s foot (Table 1).

These groups were based on codes in the high risk foot report published by NMHS Public Health & Ambulatory Care (NMHS, 2014), with two differences:

1) The codes M86.57 ‘Other chronic haematogenous osteomyelitis, ankle and foot’ and M86.67 ‘Other chronic osteomyelitis, ankle and foot’ were used instead of M83.57 ‘Other drug-induced osteomala- cia in adults, ankle and foot’ and M83.67 (does not exist).

2) The codes L03.13 ‘Cellulitis of lower limb’, L03.14 ‘Cellulitis of foot’, and L03.02 ‘Cellulitis of the toe’ were used along with L03.11 ‘Cellulitis of the lower limb’ to identify admissions with ‘Cellulitis of the Lower Limb’. The code L03.11 ‘Cellulitis of lower limb’ was disaggregated into L03.13 and L03.14 from July 1st 2015.

Table 1 Diagnosis/procedure groups comprising high risk foot admissions

Diagnosis/procedure group

Diagnoses comprising diagnosis/procedure group

Codes included (in any diagnosis/procedure

field)Codes Excluded

Amputation

Non-Traumatic Minor Am- putation: Toe 44338-00’ ‘44358-00’ (Traumatic amputation)

‘S98.0’, ‘S98.1’, ‘S98.2’,‘S98.3’, ‘S98.4’, ‘S78.0’,‘S78.1’, ‘S78.9’, ‘S88.0’,‘S88.1’, ‘S88.9’, ‘T05.3’,‘T05.4’, ‘T05.5’, ‘T13.6’

Non-Traumatic Major Am- putation: Above Knee:

44367-00’ ‘44370-00’,

‘44367-00’Non-Traumatic Major Am-

putation: Below Knee ‘44367-02’

Diabetic Foot Diabetic Foot E11.73’ ‘E10.73’ ‘E13.73’ ‘E14.73’ N/A

Peripheral Vascular Disease Peripheral Vascular Disease (PVD)

I70.20’ ‘I70.21’ ‘I70.22’ ‘I70.23’ ‘I70.24 N/A

Cellulitis Cellulitis of the Lower Limb L03.11’ ‘L03.13’ ‘L03.14’ ‘L03.02’ N/A

Osteomyelitis/Charcot’s Foot

Charcot’s Foot G98+/M14.6*’ ‘A52.1+/ M14.6*’ N/A

Osteomyelitis (of the foot / ankle)

M86.07’ ‘M86.17’ ‘M86.27’‘M86.37’ ‘M86.47’, ‘M86.57’‘M86.67’ ‘M86.87’ ‘M86.97’

N/A

Sociodemographic distribution of high risk foot admissions

Between 1st January 2002 and 30th June 2016 there were 149,639 high risk foot admissions among 69,020 different individuals, of which 60,390 admissions occurred in the last five financial years (i.e. between 1st July 2011 and 30th June 2016). The number of individuals having a HRF admission within each financial year increased from 5,269 in 2002/03 to 8,377 in 2015/16.

Within the most recent five years of available data, rates of high risk foot admissions varied between demographic sub-groups, with 22.0% occurring among individuals aged 75-84, 60.4% among males, and 9.6% among Ab- original patients (Table 2). The latter rate is approximately 3 times the proportion of Aboriginal individuals in WA (3.7% (source: ABS)).

The largest direct admission costs were observed in the admitted population of less than 55 years of age, owing to the large number of patients in this group. Admissions for high risk foot conditions represented approximately$656 million in inpatient costs between July 1st 2011 and June 30th 2016. Within this period, the average number of admissions per admitted patient was 1.9.

Table 2. Distribution of HRF admissions occurring between July 1st 2011 and June 30th 2016, among WA residents.

Demographic group Number of individuals*

Admission count(%)

Average hospital cost $thousands

(95% CI)

Total admission costs

$millions (%)<55 9240 15307 (25.3%) 10.1 (9.8-10.3) 153.9 (23.5%)

55-64 3921 9044 (15%) 12 (11.7-12.3) 108.6 (16.6%)Age group 65-74 5188 12985 (21.5%) 11.6 (11.4-11.9) 151.2 (23%)

75-84 5674 13293 (22%) 10.8 (10.6-11) 143.4 (21.9%)85+ 4457 9761 (16.2%) 10.1 (10-10.3) 98.9 (15.1%)

Male 18739 36474 (60.4%) 11.1 (11-11.2) 405.1 (61.8%)Female 12875 23916 (39.6%) 10.5 (10.3-10.6) 250.8 (38.2%)

Aboriginal Sta- non-Aboriginal 28571 54614 (90.4%) 10.8 (10.7-10.9) 591.9 (90.2%)tus Aboriginal 3030 5776 (9.6%) 11.1 (10.7-11.4) 64 (9.8%)

2011/12 7579 11284 (18.7%) 10.8 (10.6-11.1) 122.3 (18.6%)2012/13 7779 11807 (19.6%) 10.3 (10.1-10.5) 122.1 (18.6%)

Year 2013/14 7814 11692 (19.4%) 10.7 (10.5-10.9) 125.3 (19.1%)2014/15 8145 12500 (20.7%) 11.2 (11-11.4) 139.8 (21.3%)2015/16 8377 13107 (21.7%) 11.2 (10.9-11.4) 146.4 (22.3%)

Total 31601 60390 10.9 (10.8-11) 656

Note: * Represents the number of individuals with at least one admission in the five-year period, unless category is year,where this represents individuals with at least one admission in each year.

Geographic distribution of high risk foot admissions

Figure 1 maps the mean annual number of HRF admissions between 1st July 2011 and 30th June 2016 by geographic area of patient residence. The areas used here are Australian Bureau of Statistics (ABS) Statistical Area Level 2 (SA2) boundaries, which have a mean population of 10,000 individuals.

Figure 1. Mean annual number of high risk foot admissions between July 1st 2011 and June 30th 2016, by SA2 region.

Legend

No admissionsMean annual admission count: Overall HRF

1.0 - 23.223.3 - 39.639.7 - 57.0

Insert

57.1 - 79.479.5 - 151.8

0 250 500 1,000

km

Insert

Ü

Section 2. Describing high risk foot sub- groups


It is important to examine the distribution of HRF admissions between sub-groups of admission type. This information can have implications for health service delivery and planning, since each may have a different trajectory of health care utilisation.

The number of admissions increased over time within all sub-groups (except PVD), with the increase greatest in cellulitis. In contrast PVD was generally decreasing in recent years. The opposing trends of cellulitis and PVD might be indicative of true shifts in the burden of disease, or might reflect changing coding practices.

Cellulitis of the lower limb and PVD were the most common type of admission within high risk foot admissions, with 55.6% of high risk foot admissions associated with cellulitis (33,579 out of 60,390 admissions) and 30.9% of admissions associated with PVD (18,631 out of 60,390 admissions).

The proportion of admissions within high risk foot sub- groups varied socio-demographically. For example, admissions for PVD occurred primarily in older patients and among males, with approximately 80% of PVD admissions occurring in patients aged 65 or above and 65.7% among males. A relatively high proportion of diabetic foot admissions was represented by Aboriginal patients (16.9%), compared to PVD where just 3% were in Aborig- inal patients.

The geographic distribution of admissions for high risk foot sub-groups can suggest the type of high risk foot admission that can be expected at different sites. SA2s nearby Fremantle, Rockingham, Armadale, and Bentley consistently had relatively high numbers of HRF admissions (of all types) compared to other areas. In regional areas, the Kimberley area had relatively high numbers of all types of HRF admission except PVD.

There is some overlap in sub-groups of with high risk foot admissions since such admissions are identified using diagnoses (principal and additional) and procedures. Among the 69,020 unique patients with at least one high risk foot admission between January 1st 2002 and June 30th 2016, 57,641 (83.5%) had an admission in only one of the five HRF sub-groups, with 70.4% of these represented by the cellulitis sub-group. This number corresponds to 58.8% of the overall denominator of 69,020 unique patients.

High risk foot sub-groups

This section examines temporal and sociodemographic differences in high risk foot admissions by sub-groups. These sub-groups are: i) cellulitis of the lower limb (hereafter: cellulitis); ii) peripheral vascular disease (PVD); iii) non-traumatic amputations of the toe, below the knee, or above the knee (hereafter: amputation); iv) diabetic foot; and v) osteomyelitis of the foot or ankle/Charcot’s foot (hereafter osteomyelitis/Charcot’s foot). Refer to Ta- ble 1 for further detail on diagnosis/procedure codes for these sub-groups. It is important to examine geographical and demographic differences in admissions rates between high risk foot sub-groups, to help guide policy and planning for health service delivery.

The number of admissions increased over time within all sub-groups except PVD. This increase was greatest for cellulitis, in contrast to an overall decreasing trend in PVD. The opposing trends of cellulitis and PVD might be indicative of true shifts in the burden of disease, or might reflect changing coding practices; an experienced medical coder might be able to assist in explaining this observation. Note that only the admission counts over time are shown in Figure 2, and not admissions as a rate within a population.

Temporal distribution of high risk foot admissions by diagnosis sub-group

Figure 2 shows the number of admissions in each sub- group of high risk foot, each financial year from 2002/03 to 2015/16. These admissions included relevant principal and additional diagnoses. No sudden changes in admission counts within sub-groups were readily apparent; such changes could indicate systematic changes in coding at specific points in time.

Figure 2. Counts of admissions over time by sub-group of high risk foot

Sociodemographic distribution of high risk foot admissions by diagnosis sub-group

Table 3 shows the percentage of high risk foot admissions occurring between 1st July 2011 and 30th June 2016, separated by admission sub-group and demographic groups (age group, sex, Aboriginal status). Some overlap occurs where individuals had multiple diagnoses and procedures for a single admission.

There were 60,390 HRF admissions in the last five years. Cellulitis and PVD were the most common sub-groups within high risk foot admissions, with 55.6% of high risk foot admissions associated with cellulitis (33,579 out of 60,390) and 30.9% of admissions associated with PVD (18,631 out of 60,390).

Within each sub-group, the proportion of admissions represented by different age groups varied, and similarly for the proportion of admissions occurring among Aborig-

inal patients and among males (compared to females). For example, admissions for PVD occurred primarily in older patients and among males, with approximately 80% of PVD admissions in patients aged 65 or above and 65.7% in males. In cellulitis 34.8% of admissions were in patients aged below 55. A relatively high proportion of diabetic foot admissions was represented by Aboriginal patients (16.9%), compared to 3% of PVD admissions and approximately 10% of the other diagnoses.

The distribution of patient age within each sub-group of high risk foot admissions is illustrated graphically in Fig- ure 3.

Table 3. Percentage distribution of HRF admissions among WA residents, by sociodemographic and admission sub-groups. Data is for admissions occurring between July 1st 2011 and June 30th 2016.

High risk foot sub-group

Demographic groupAmputa- Diabetic

Cellulitis PVD OM/Charcottion foot Overall HRF

Age group

< 55 23.42 22.06 34.77 6.02 28.78 25.35

55-64 20.31 22.04 13.84 14.23 20.98 14.98

65-74 25.42 27.07 16.14 29.43 23.92 21.5

75-84 20.42 19.61 18.49 30.6 16.92 22.01

85+ 10.43 9.22 16.76 19.72 9.4 16.16

Sex Male 67.29 69.13 55.64 65.69 66.21 60.4

AboriginalAboriginal 10.46 16.89 10.8 3 13.85

status9.56

Total admissions 3,442 11,586 33,579 18,631 5,034 60,390

Note: percentages are column percentages within each demographic group (i.e. age group, sex, Aboriginal status)

Figure 3. Age distribution of patients by HRF sub-group.

Comorbidity of high risk foot diagnoses

As noted above, there is some overlap in admissions with high risk foot sub-groups, since such admissions are identified using both diagnoses (principal and additional) and procedures. Table 4 shows the number of unique individuals with common combinations of high risk foot admissions between January 1st 2002 and June 30th 2016. Among the 69,020 unique patients with at least one high risk foot admission in that time period, 57,641 (83.5%) had an admission in only one of the five high risk foot sub-groups over that time period, with 70.4% of these represented by the cellulitis sub-group. This number corresponds to 58.8% of the overall denominator of 69,020 unique patients.

Table 4 shows that individuals with both cellulitis and PVD admissions comprised 24.1% of individuals with more than one type of high risk foot admission, but only 4.0% of all individuals with a high risk foot admission. Other sub-group combinations were less commonly represented.

Geographic distribution high risk foot admissions by diagnosis sub-group

The geographic distribution of admissions for high risk foot sub-groups can suggest the type of high risk foot admission expected in different regions and at different clinical sites. This may have implications for health service planning and delivery. Figure 4-8 map the mean annual number of admissions within the sub-groups of amputation, cellulitis, diabetic foot, PVD, and osteomyelitis/ Charcot’s foot, based on SA2 boundaries and data from July 1st 2011 to June 30th 2016.

The distribution of admissions varied between high risk foot sub-groups. However, some features are common to each map: SA2s nearby Fremantle, Rockingham, Arma- dale, and Bentley consistently had relatively high numbers of admissions compared to other areas. In regional areas, the Kimberley area had relatively high numbers of all types of HRF admission except PVD.

Table 4 Most common high risk foot sub-group combinations among unique individuals.

uals (denomina-combination

NNo. high riskUnique individ-

Most common % of sub-group % of overallfoot diagnoses tor) denominator denominator

1 57641 Cellulitis only 40555 70.4 58.8

2+ 11379 Cellulitis, PVD 2746 24.1 4.0

3+ 5065 All sub-groups 946 19.1 1.4

4+ 2560 All sub-groups 946 37.6 1.4

5 966 All sub-groups 946 100 1.4

Figure 4 Mean annual number of amputation admissions between 2011/12 and 2015/16, by SA2 region.

Legend

No admissionsMean annual admission count: Amputations

0.2 - 1.21.3 - 2.42.5 - 3.6

Insert

3.7 - 5.05.1 - 9.8

0 250

Ü500 1,000

km

Insert

Figure 5 Mean annual number of cellulitis admissions between 2011/12 and 2015/16, by SA2 region.

Legend

No admissionsMean annual admission count:Cellulitis

1.3 - 12.412.5 - 22.022.1 - 30.2

Insert

30.3 - 41.641.7 - 85.2

0 250 500 1,000

km

Insert

Ü

Figure 6 Mean annual number of diabetic foot admissions between 2011/12 and 2015/16, by SA2 region.

Legend

No admissionsMean annual admission count:Diabetic foot

0.2 - 3.63.7 - 7.47.5 - 11.0

Insert

11.1 - 16.616.7 - 41.6

0 250 500 1,000

km

ÜInsert

Figure 7 Mean annual number of PVD admissions between 2011/12 and 2015/16, by SA2 region.

Legend

No admissionsMean annual admission count:PVD

Insert

0.2 - 7.47.5 - 13.413.5 - 19.019.1 - 26.626.7 - 60.6

0 250 500 1,000

km

ÜInsert

Figure 8 Mean annual number of osteomyelitis/Charcot’s foot admissions between 2011/12 and 2015/16, by SA2 region.

Legend

No admissionsMean annual admission count:OM/ Charcot's foot

0.2 - 1.81.9 - 3.23.3 - 5.0

Insert

5.1 - 7.67.7 - 18.2

0 250 500 1,000

km

ÜInsert

Section 3. Longitudinal hospital demand associated with cardinal high risk foot admissions


Cardinal HRF admissions are defined here as the first HRF admission following a period of at least three years without a HRF admission. Such admissions can represent the first event in an episode of hospitalisation for an individual. There were 61,578 cardinal high risk foot admissions between January 1st 2005 and June 30th 2016.

At any one point in time, approximately 800 hospital beds per day are occupied by individuals who have experienced a cardinal high risk foot admission in the last 10 years (i.e. almost 9% of approximately 9,000 available hospital beds in WA). This calculation excluded inpatient days associated with dialysis. Similar results have been reported in Queensland (Lazzarini et al., 2016).

An overall ratchet increase in mean hospital costs and mean inpatient demand following a cardinal admission was observed. This ratchet was most pronounced in patients aged 55-64, where mean annual hospital costs increased by approximately $5,000 (from $2,000 to $7,000) following a cardinal admission and inpatient days increased by approximately 4 days per person-year (from 3 to 7). On average, the ratchet increase in cost is sustained for at least ten years after the cardinal admission.

Similar results were observed within sub-groups of high risk foot admission types, but the ratchet increase in inpatient demand was lower for cellulitis and PVD compared to the other sub-groups. Differences in mean hospital demand between sub-groups of high risk foot admissions were most evident in the two-years following a cardinal admission. These figures were: cellulitis 19.9 days per person-year; PVD 28.9; amputation 49.1; osteomyelitis/Charcot’s foot 48.2; and diabetic foot 54.9 days per person-year. Corresponding figures for hospital cost ($1,000s) were: cellulitis $16.8; PVD $26.1; amputation$36.3; osteomyelitis/Charcot’s foot $37.4; and diabetic foot $43.3.

By comparing costs before and after a cardinal admission, it could be speculated that cost savings of up to$40,000 over six years might be attained by preventing cardinal high risk foot admissions within the relatively high cost types of amputation, osteomyelitis/Charcot’s foot, or diabetic foot.


Defining cardinal high risk foot admissions

The analyses presented here rely on the concept of a cardinal high risk foot admission, defined here as the first HRF admission following a period of at least three years without a HRF admission. Such admissions can represent the first instance in an episode of utilisation of health services by an individual. The first possible date for a cardinal admission in the current data is January 1st 2005 (due to the 3-year clearance period).

Cardinal admissions for different types of HRF admission can also be defined. In this case, defined as the specific type of HRF admission (e.g. diabetic foot) following a period of three years without that specific type of HRF admission.

Hospital beds occupied by individuals who have had a cardinal high risk foot admission in the past

The longitudinal demand on hospital resources by high risk foot patients can be estimated by observing the number of hospital beds occupied by individuals with a previous cardinal HRF admission. This data is shown in Figure 9. In January 2005 the number of individuals with a previous cardinal high risk foot admission cannot be identified, and so the number of beds occupied by such individuals is zero. However, the number of beds occupied increases over time as individuals are identified, until it begins plateauing around January 2015. At this point, the number of individuals entering the cohort is balanced by those dying or leaving WA. This calculation excluded inpatient days associated with dialysis.

Figure 9. Cumulative hospital beds occupied (per day) by individuals with a previous cardinal high risk foot admission.

This plateauing of number of individuals with a previous cardinal high risk foot admission represents a steady state of total hospital beds occupied at any one time. In December 2015 approximately 800-900 hospital beds per day were occupied by individuals with a cardinal high risk foot admission occurring after January 2005. This suggests that at any one point in time almost 10% of approximately 9,000 WA hospital beds are occupied by individuals who have experienced a cardinal high risk foot in the last ten years. Similar results have been reported in Queensland (Lazzarini et al., 2016).

Estimating longitudinal hospital demand of patients with cardinal high risk foot admissions

This section describes the mean utilisation of hospital resources by patients with cardinal high risk foot admissions, measured in terms of inpatient bed days and hospital costs, at time points relative to the cardinal admission. Utilisation of hospital resources can be represented by all-cause hospital bed days occupied as well as costs associated with all-cause inpatient days and all-cause emergency department (ED) presentations.

Estimating hospital costsEach financial year, the Independent Hospital Pricing Authority releases a cost report for Diagnostic Related Groups (inpatients), Urgency Related Groups (ED presentations), and their average estimated cost for that financial year. This information is based upon a sample of par- ticipating hospitals that provide cost information.

The estimated DRG cost for each hospital separation is assigned according to the DRG and the financial year of the separation date, where all costs are derived from public hospital estimates. The estimated costs of emergency department presentations were based upon triage category and admission status, as indicated by cost re- ports released each year. All costs were standardised to the 2014 Australian dollar using the AIHW’s health infla- tion index (AIHW, 2015). These costs are indicative of the average cost for each admission or ED presentation type, and may not reflect the true costs to the hospital system.

The DRG cost of each admission is distributed between the admission and separation dates relative to a cardinal admission for HRF, where a cardinal admission is defined as the first admission for a particular disease type within a period of three years. Bootstrapped LOESS regression has been applied to the daily rate of inpatient and ED costs for the cardinal event cohort to obtain a stable and smoothed estimate of the average cost in the periods leading up to and following a cardinal admission.

CHASM 25

Mean longitudinal hospital costs among patients with cardinal high risk foot admissionsFigure 10 shows the mean longitudinal hospital costs (measured as inpatient costs plus ED presentation costs, per person-year) associated with patients experiencing high risk foot admissions. Analogous data separated by age group is shown in Figure 11. These plots illustrate an overall upwards ratchet in mean hospital costs among cardinal high risk foot patients following the date of their cardinal admission. However, Figure 11 indicates that this ratchet is driven by younger patients, in particular those aged 55-64, in contrast to patients aged 75 or above where no increase in mean hospital costs is observed following the date of their cardinal high risk foot admission. In patients aged 55-64, mean hospital costs increased from approximately $2,000 per person-year in the years leading up to their cardinal high risk foot admission to approximately $7,000 in the years following their cardinal admission (Figure 11).

Mean longitudinal inpatient days among patients with cardinal high risk foot admissions Similar effects were observed with respect to mean longitudinal inpatient demand. Figure 11 illustrates an overall upwards ratchet in mean inpatient demand (measured as bed days occupied, per person-year), in the years following the cardinal high risk foot admission date. However, as with hospital costs, Figure 14 shows that this effect is driven by an upwards ratchet in the younger age groups, while in contrast a downwards ratchet is observed in older patients. This effect was most pronounced in the 55- 64 age group, where mean inpatient demand increased from approximately 3 bed days occupied per person-year in the years leading up to the cardinal admission to approximately 7 bed days occupied per person-year following the cardinal admission (Figure 13). Note that inpatient days associated with dialysis were included in this analysis (Figures 12-14; and Table 6) and represent 10% of all inpatient days amongst patients with a HRF admission.

Figure 10. Mean longitudinal hospital costs (measured as inpatient bed day costs plus ED presentation costs) associated with patients with cardinal high risk foot admissions

Figure 11. Mean longitudinal hospital costs (inpatient bed day costs plus ED presentation costs, per person-year) associated with patients with cardinal high risk foot admissions. Data is shown separated by age-group (<55, 55-64, 65-74, 75-84, 85+)

Figure 12. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions

Figure 13. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions. Data is shown separated by age group (<55, 55-64, 65-74, 75-84, 85+).


Mean longitudinal hospital demand among diagnosis sub-groups of cardinal high risk foot admissionsResults for cardinal admissions within each sub-group comprising high risk foot admissions are presented here. Table 5 shows the mean number of hospital bed days occupied by patients with cardinal admissions in a high risk foot sub-group, in time periods relative to the date of the cardinal admission. For example, among patients experiencing cardinal high risk foot admissions with a cellulitis diagnosis, the mean number of hospital bed days occupied was 7.7 days (95% CI: 7.4-8.1) in the time period 5-6 years prior to the date of their cardinal admission (Table 5). Note that in Table 5 the ‘cardinal period’ is defined by a 30-day period either side of the date of the cardinal admission. The data in Table 5 is illustrated graphically in Figure 14 and Figure 16.

The most common sub-groups of cardinal high risk foot admissions, i.e. cellulitis and PVD (refer to Section 2), are associated with fewer hospital bed days occupied across the study period (Table 5; Figure 14). These sub-groups can be considered less severe conditions within patients with high risk foot. For example, the mean number of hospital bed days occupied by patients with a cardinal cellulitis admission was 13.9 bed days per person-year (95% CI: 13.5-14.3) in the 1-2 year period prior to the date of the cardinal admission, while for PVD this value was 18 (17.2- 18.8) in the same time period. In comparison, the corresponding values for patients with cardinal admissions for amputation, diabetic foot, and osteomyelitis/Charcot’s foot were much greater before and after the date of their cardinal admissions. For example, in diabetic foot, mean hospital bed days were 29.9 (28.2-31.7) in the 1-2 years prior to the date of the cardinal admission, increasing to54.9 (52.7-57.1) in the 1-2 year period following the cardinal admission (Table 5; Figure 14).

Similar results were observed for mean hospital costs before and after the date of a cardinal high risk foot admission, as shown in Table 6. The data in Table 6 is illustrated graphically in Figure 15 and Figure 17.

The difference in hospital use between the less severe (i.e. PVD and cellulitis) and severe (i.e. amputation, diabetic foot, and osteomyelitis/Charcot’s foot) cardinal high risk foot admissions is most evident in the two year period im- mediately following the cardinal event; here, the average patient had 19.9, 28.9, 49.1, 48.2, and 54.9 inpatient days per person-year for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot and diabetic foot, respectively (Table 5; Figure 14). In terms of total inpatient and ED presentation costs, the corresponding figures for the five high risk foot sub-groups were $16.8k, $26.1k, and $36.3k, $37.4k,$43.3k over the same two year period (Table 6; Figure

15). By comparing costs before and after a cardinal admission, it could be speculated that cost savings of up to

CHASM 29

$40,000 over six years might be attained by preventing cardinal high risk foot admissions within the relatively high cost types of amputation, osteomyelitis/Charcot’s foot, or diabetic foot.


Table 5 Mean number of hospital bed days occupied by patients with a cardinal high risk foot admission, in time periods relative to the date of the cardinal admission

Time period (relative to cardinal HRF admission)Prior Following

HRF sub- group 5-6 yrs 3-4 yrs 1-2 yrs period 1-2 yrs 3-4 yrs 5-6 yrs

Cellulitis7.7

(7.4-8.1)9.9

(9.6-10.3)13.9

(13.5-14.3)8.9

(8.8-9.1)19.9

(19.3-20.4)13.4

(12.8-14)10.7

(10-11.3)

PVD9.3 12.4 18 9.1 28.9 18.4 14.4

(8.8-9.9) (11.7-13.1) (17.2-18.8) (8.9-9.3) (28-29.7) (17.6-19.3) (13.5-15.3)

Amputa- 15.4 20.7 34.9 20.2 49.1 26.7 20.9tions (14.3-16.7) (19.4-22.2) (33.1-36.7) (19.8-20.7) (46.8-51.2) (24.6-28.7) (18.8-23.2)

OM/C14.2 18.5 30.2 20.3 48.2 31.3 23.6

(12.8-15.6) (16.9-20.1) (28.1-32.1) (19.9-20.8) (45.5-50.8) (28.9-33.8) (20.9-26.3)

Diabetic 16.4 21 29.9 18.2 54.9 34.5 26.8Foot

(15.1-17.8) (19.6-22.5) (28.2-31.7) (17.8-18.6) (52.7-57.1) (32.3-36.8) (24.4-29.3)

Table 6 Mean hospital cost ($K AU 2014) per patient with a cardinal high risk foot admission, in time periods relative to the date of the cardinal admission

Time period (relative to cardinal HRF admission)

Prior Following

HRF sub- group 5-6 yrs 3-4 yrs 1-2 yrs

Cardinalperiod 1-2 yrs 3-4 yrs 5-6 yrs

Cellulitis7.4

(7.2-7.7)

9.3

(9-9.5)

13

(12.6-13.3)

8.3

(8.3-8.5)

16.8

(16.4-17.2)

12.5

(12.1-12.9)

10.5

(10.1-11)

PVD9.5

(9.1-10)

11.7

(11.2-12.2)

16.9

(16.3-17.5)

10.5

(10.3-10.7)

26.1

(25.5-26.9)

18.1

(17.4-18.7)

14.5

(13.9-15.2)

Amputa- 13.9 18 29.2 19.6 36.3 23.3 18.4tions (13-14.8) (17-18.9) (28-30.4) (19.3-20) (34.9-37.7) (21.8-24.8) (17-20.1)

OM/C13.4

(12.4-14.5)

17.1

(16-18.1)

26.9

(25.5-28.3)

18.1

(17.7-18.5)

37.4

(35.6-39.3)

27.6

(25.9-29.5)

22

(20-24.2)

Diabetic 15 18.4 26 17.3 43.3 29.6 23.7Foot (13.9-15.9) (17.2-19.5) (24.7-27.2) (16.9-17.7) (41.7-44.7) (27.9-31.3) (22-25.4)

CHASM 31

Figure 14. Mean longitudinal inpatient demand (measured as bed days occupied) among patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/ Charcot’s foot, and diabetic foot

Figure 15. Mean longitudinal hospital costs (measured as inpatient bed day costs plus ED presentation costs) associated with patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot, and diabetic foot

Figure 16. Mean longitudinal inpatient demand (measured as bed days occupied, per person-year) among patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot, and diabetic foot

Figure 17. Mean longitudinal hospital costs (inpatient bed day costs plus ED presentation costs, per person-year) associated with patients with cardinal high risk foot admissions for cellulitis, PVD, amputation, osteomyelitis/Charcot’s foot, and diabetic foot

CHASM 33

Section 4. Forecasting areas with high rates of high risk foot admissions


Similarly, optimal metropolitan models forecast the following areas to have higher than average age-sex standardised rates of HRF admissions between 3 and 5 years into the future:

It is of interest to predict areas with standardised rates of admissions persistently above average in future time periods to support clinical service planning.

Optimal metropolitan models had positive predictive

Armadale - Wun- gong - Brook-

daleBalga - Mirra-

booka

Rivervale - Kew- dale - Cloverdale

Maylands Willagee

value (PPV) greater than 60%, except for the 5-year model (PPV = 39.7% (95% CI: 29.1-50.0)). The state-wide models had a PPV greater than 85% while maintaining sensitivity above 30%, except for the 5-year model (PPV = 75.7%

Bentley - Wilson- St James

Calista

Midland - Guild- ford

Nollamara - Westminster

Lockridge - Kiara

Belmont - Ascot- Redcliffe

(60.0-88.6)). Sensitivity represents the proportion of SA2s that were correctly predicted out of those that had high risk foot admission rates persistently above average in the relevant future time period (i.e. the proportion of persistently above average areas predicted). Positive predictive value represents the proportion of SA2s correctly predicted to have above average high risk foot admission rates in the future time period, using a denominator of all positively predicted SA2s (i.e. the proportion of predictions which are correct). It is important to balance the sensitivity and PPV achieved by prediction models when planning health interventions.

Even when PPV was relatively low, a large proportion of SA2s predicted positive by the optimal models had standardised rates of high risk foot admissions that were greater than the overall state rate of admissions in the final year of the forecast period.

Optimal state-wide models forecast the following regional areas to have higher than average age-sex standardised rates of HRF admissions between 3 and 5 years into the future (i.e. up until the 2018/19 financial year and possible up to the 2020/21 financial year):

Broome Kununurra Geraldton - EastCarnarvon Narrogin Roebuck

Derby - West Kimberley

Gnowangerup South Hedland

Geraldton Kalgoorlie Halls Creek York - Beverley

Cannington - Queens Park Parmelia - Orelia

Girrawheen

Describing geographical inequity

Observing the geographical distribution of high risk foot admissions (refer to Section 1) can show where high risk foot patients reside, but it cannot describe area-level inequality. Population rates, adjusted for varying age-sex distributions between SA2s, can show which populations are over-burdened by high risk foot admissions. Howev- er, it is known that admission rates can vary from year to year (Grattan Institute, 2016). Therefore, it is of interest to predict areas with standardised rates of admissions that are persistently higher than on average across the state, in future time periods.

This section describes statistical prediction models de- veloped to forecast geographic areas (SA2s) with higher than average age-sex standardised rates (ASSRs) of high risk foot admissions in three future time periods, namely: 2016-17 to 2018-19 (3-year period), 2016-17 to 2019-20 (4-year period), and 2016-17 to 2020-21 (5-year period).

Identifying hotspots of high risk foot admissions

Age-sex standardised rates of high risk foot admissions were calculated using the direct standardisation ap- proach, with reference to the Australia-wide population in each age-sex stratum (taken from the 2011 ABS cen- sus). Within each financial year, SA2s with above average ASSRs of high risk foot admissions were classified as ‘hot’, and this information was subsequently used to define a dichotomous outcome with value ‘1’ for SA2s that were ‘hot’ throughout a future outcome period and ‘0’ for other SA2s. This construction was carried out in preparation for the training and validation of predictive models forecasting in (unseen) future time periods.

Candidate predictors

The prediction models utilised data for all individuals with high risk foot admissions, aggregated by financial year (2002-03 to 2015-16 inclusive) and SA2. Any SA2 with a population less than 1,000 in any financial year was excluded, leaving 149 metropolitan SA2s and 222 state- wide SA2s. Excluded SA2s predominantly comprised national parks, industrial areas, lakes, and airports.

Potential (SA2-level) predictors included:i) The proportion of male individuals (centred

around the mean within each year),ii) The proportion of the population identifying as

Aboriginal,iii) Rurality (metropolitan or regional),iv) Distance to nearest ED, GP clinic, and MH unit

(measured in kilometres),

v) A percentile rank for each of four socio-economic indices for areas (SEIFAs), namely: a) Index of Rela- tive Socio-Economic Disadvantage (IRSD), b) Index of Relative Socio-Economic Advantage and Disad- vantage (IRSAD), c) Index of Education and Occu- pation (IEO), and d) Index of Economic Resources (IER), and

vi) Past persistence of higher than average presentation rates, measured as either: a) the number of prior consecutive years with higher than average rates, or b) the weighted average number of years out of the past five years with higher than average rates (5 points for the most recent year, 4 points for the second most recent year, and so on).

Interaction terms between each of the four SEIFA indices and a) the past persistence predictors, and b) distance to nearest ED, were also considered. Information for calcu- lating distance to GP clinics was obtained from the WA Primary Health Alliance and was current in 2015. The distance predictors were log-transformed to account for skewness.

Metropolitan and state-wide models

Optimal metropolitan and state-wide models were obtained using a modified all-subset selection design (Tu- son et al., submitted 2017). All candidate models were fit- ted with the Firth correction and models with more than 10 terms were not considered to avoid problems arising due to data sparsity. A total of 3,959 metropolitan models and 7,887 state-wide models were considered in se- lecting optimal models. Model validation was conducted using repeated, stratified, five-fold cross-validation. This process required efficient utilisation of high-capacity par- allel computing.

Following the validation process, the optimal state-wide and metropolitan models were applied to recent data (from the 2015-16 financial year) to forecast SA2s expected to have persistently high rates of high risk foot admissions in the 3-year, 4-year, and 5-year future time periods.

Results

Important predictors

Table 8 Predictors in the optimal state-wide models.

The predictors in the optimal 3-year, 4-year, and 5-year metropolitan and state-wide models are listed in Table 7 and Table 8. Historically high rates of HRF admissions and socioeconomic variables were often predictive of future high rates, and similarly for the proportion of the

Forecast horizon

IER percentile

Predictors

population identifying as Aboriginal. Other predictors such as distance to nearest ED (km) were less consistently present. Note that observing important predictors in the optimal models can help guide the selection of candidate predictors for future models. However, these predictors are selected to optimise predictive power rather than examine association, and therefore should not be interpreted in the latter sense.

Table 7 Predictors in

the optimal metropolitan

models.

# of past consecutive years with above average presentation rates

3-years

Proportion of popu

lation identifying as Ab- originalProportion of male individuals

Proportion of population aged 55 or above Distance to nearest ED (km)

# of past consecutive years with above average presentation rates

IER percentile

Forecast horizon

IER percentile

Predictors

4-years

Proportion of male individualsProportion of population aged 55 or above, with quadratic termDistance to nearest ED (km)

3-years

4-years

Proportion of population identifying as Ab- originalProportion of population aged 55 or above

Distance to nearest ED (km) Distance to nearest GP clinic (km)

Proportion of population identifying as Ab-

5-years

Weighted avg. # of past years with above average presentation rates

IER percentileProportion of population identifying as Ab- original

Distance to nearest ED (km) Interaction: distance to nearest ED (km) x

original IER percentile

5

-years

IEO percentileProportion of population identifying as Ab- origi

nalInteraction: IEO percentile x # of past consecutive y

Accessibility to GP clinic index percentile (gravity model)

Weighted avg. # of past years with above average presentation rates

Interaction: distance to nearest ED (km) x IER percentile

IRSAD percentile

# of past consecutive years with above average presentation rates

ears with above averag

e presentation rates

Distance to nearest ED (km)

Performance of optimal modelsTable 9 shows results from the optimal metropolitan and state-wide models. While maintaining a minimum sensitivity of 30% the optimal metropolitan models had positive predictive value (PPV) greater than 60%, except for the 5-year model which had a PPV of 39.7% (95% CI: 29.1-50.0). The state-wide models had a PPV greater than 85% while maintaining sensitivity above 30%, except for the 5-year model which had a PPV of 75.7% (60.0-88.6).

Sensitivity represents the proportion of SA2s that were correctly predicted out of those that had high risk foot admission rates persistently above average in the relevant future time period. Positive predictive value represents the proportion of SA2s correctly predicted to have above average high risk foot admission rates in the future time period, using a denominator of all positively predicted SA2s (i.e. the proportion of correct predictions). It is important to balance the sensitivity and PPV achieved by prediction models when planning health interventions.

Table 9 Pooled validation statistics for the optimal 3-year (2016-19), 4-year (2016-20), and 5-year (2016-21) metropolitan and state- wide prediction models.

SRs ratios of admissions among SA2s predicted positive by each of the optimal metropolitan model (Figure 18) and state-wide models (Figure 19). The small medium grey bars overlaying the light histogram indicate what proportion of all SA2s were predicted positive in each in- terval. These proportions are always small, since despite the reasonable sensitivity of the models, most SA2’s do not have rates significantly higher than the state rate.

These values correspond to the final year of a validation period used to identify the optimal models. They show that even when PPV is relatively low, as in the optimal 5-year metropolitan model (PPV = 39.7%; Table 9), a large proportion of SA2s predicted positive by the optimal models had ASSRs of high risk foot admissions that were greater than the overall state rate of admissions in the final year of the forecast period (i.e. the ASSRs of high risk foot admissions in these SA2s were greater in magnitude than the state rate of admissions at the end of the forecast period, even if not statistically significantly greater). This suggests that the optimal models presented here may still be relied upon to guide health intervention planning, even when PPV estimated for the models is low.

Optimal model statistics

FSensitivity threshold

Sensitivity(LCL-UCL)

PPV(LCL-UCL)

3 years 31.1 64.4(20.0-40.0) (42.9-85.2)

Metro- politan 4 years 0.3 37.0

(25.0-50.0)84.6

(60.0-100)

5 years 79.2 39.7(64.5-80.0) (29.1-50.0)

3 years 37.9 99.1(33.3-41.7) (90.0-100)

State- wide 4 years 0.3 30.5

(12.5-43.8)89.1

(67.4-100)

5 years 60.5 75.7(45.5-79.8) (60.0-88.6)

Shown below are bar

plots indicating the distribution of ASSRs of high risk fo

Region

Figure 18 Ratio ASSRs of HRF admissions (vs metropolitan mean rate) among SA2s (left to right) 3-year, 4-year, and 5- year metropolitanmodels.

0 1 2 3 4

ASSR ratio

0 1 2 3 4

ASSR ratio

0 1 2 3 4

ASSR ratio

Figure 19 A Ratio ASSRs of HRF admissions (vs state mean rate) among SA2s (left to right) 3-year, 4-year, and 5- year state models.

0 1 2 3 4 0 1 2 3 4 0 1 2 3 4

ASSR ratio ASSR ratio ASSR ratio

Denominator: positively predicted SA2s

Denominator: all SA2s





2040

Per

cent

age

of

SA

2s20

40

Per

cent

age

of S

A2s

2040

Per

cent

age

of S

A2s

200

200

200

200

200

200







Per

cent

age

of

SA

2s20

40

Per

cent

age

of

SA

2s20

40

Per

cent

age

of S

A2s

2040


Metropolitan forecastsThe SA2s forecast by the metropolitan models are listed in Table 10, and mapped in Figure 20. A total of 14 metropolitan SA2s were forecast by at least one of the optimal models, of which 4 were forecast by at least two of the models and 2 were forecast by all three models.

Table 10 Areas forecast by the optimal metropolitan models

Forecast horizonSA2 3-year 4-year 5-year

Balga - Mirrabooka 1 1 1

Bentley - Wilson - St JamesCalista 1

Cannington - Queens Park

Girrawheen 1Maylands 1

Midland - Guildford 1

Parmelia - Orelia 1 1

Willagee 1

Lockridge - Kiara 1

Belmont - Ascot - Red-cliffe

111Armadale - Wungong -

Brookdale

1

1

1Nollamara - Westmin-

ster

11Rivervale - Kewdale -

Cloverdale

1

CHASM 39

Figure 20 Areas forecast by at least one of the optimal metropolitan models.

Balga - Mirrabooka Nollamara - Westminster

GirrawheenLockridge - Kiara

Midland - Guildford

Maylands

Bentley - Wilson - St James

Belmont - Ascot - Redcliffe

Rivervale - Kewdale - Cloverdale

Cannington - Queens Park

Willagee

Armadale - Wungong - Brookdale

Parmelia - Orelia

Calista

Ü0 5 10 20 30

km

Regional forecastsThe regional SA2s forecast by the state-wide models are listed in Table 11, and mapped in Figure 21. There were 13 regional SA2s forecast by at least one of the optimal models, of which 7 were forecast by at least two of the models and 6 were forecast by all three models.

Table 11 Areas forecast by the optimal state- wide models.

Forecast horizon

SA2 3-year 4-year 5-year

Broome 1 1 1

Carnarvon 1 1 1

Derby - West Kimberley 1 1 1

Geraldton 1 1 1

Halls Creek 1 1 1

Kununurra 1 1 1

Narrogin 1 1

Gnowangerup 1

Kalgoorlie 1

York - Beverley 1

Geraldton - East 1

Roebuck 1

South Hedland 1

Note: metropolitan areas forecast by the state-wide models are not listed.

Figure 21 Areas forecast by at least one of the optimal state-wide models.

Kununurra

Broome

Derby - Kimberley

South HedlandRoebuck

Halls Creek

Carnarvon

Geraldton

Geraldton - East

Kalgoorlie

York - Beverley

Narrogin

Gnowangerup

0 125

Ü250 500

km

Section 5. Identifying areas for targeted intervention


Analyses in Section 4 identified 27 SA2s forecast to have a disproportionately high rate of high risk foot admissions among residents, including 14 metropolitan SA2s and 13 regional SA2s. Among the 14 metropolitan SA2s forecast, Balga – Mirrabooka is identified as a potential priority area for a place-based podiatry service intervention, due to having a relatively poor index of accessibility to podiatry services combined with a relatively high population of individuals admitted with high risk foot diagnoses. Other potential priority areas may be identified.

Potential cost savings arising from prevention of cardinal high risk foot admissionsFigure 22 shows the number of cardinal high risk foot admissions among residents of the 27 areas forecast by the optimal prediction models (refer to Section 4). Based on this data, approximately 1,000 cardinal admissions per year are expected among these individuals.

The majority (66%) of these cardinal admissions will have a relatively low severity diagnosis (i.e. cellulitis (44%) or PVD (22%), refer to Section 3). However, by comparing costs before and after a cardinal admission, it could be speculated that cost savings of up to $40,000 over six years might be attained by preventing cardinal high risk foot admissions within the relatively high cost types of amputation, osteomyelitis/Charcot’s foot, or diabetic foot (see Section 3).

Figure 22 Number of cardinal high risk foot admissions per year in the 27 forecast potential priority areas.

Accessibility to podiatry health servicesFigure 23 shows current full-time equivalent (FTE) data for podiatry services in the metropolitan region (2017). Geographical boundaries shown are Statistical Local Ar- eas (SLAs). The greatest single location of available podiatry services FTE is at Sir Charles Gairdner Hospital.

Figure 24 maps the mean annual number of individuals admitted with a high risk foot diagnosis between July 1st 2011 and June 30th 2016 (i.e. over the last 5 years), by SA2 region. The areas Riverdale – Kewdale – Cloverdale, Bay- swater – Embleton – Bedford, Dianella, Armadale – Wun- gong – Brookdale had the highest mean annual admitted population, with greater than 80 individuals admitted with a high risk foot diagnoses per year on average.

Figure 25 maps the mean percentile index of accessibility to podiatry services between July 1st 2011 and June 30th 2016, by SA2 region. The index was

calculated based on a gravity model at the level of SA1 geographical regions and subsequently aggregated to SA2 level using popula-

tion weights. The gravity model takes into account both the demand (here: the population of individuals admitted with high risk foot diagnoses) and the supply (here: available FTE) of podiatry services, and includes a spec- ification of travel impedance (frictional effect of space: more weight is given to opportunities near to the origin).

To supplement the data shown in Figure 24 and Figure 25, Figure 26 compares the mean population of residents of each SA2 region admitted with a high risk foot diagnosis to the mean percentile index of accessibility to podiatry services of that SA2. Each point represents an individual SA2 region in metropolitan Perth. Additionally, SA2s forecast to have disproportionately high rates of high risk foot admissions among residents in future time periods are shown in red (refer to Table 10, Section 4 for a list of forecast metropolitan areas). Among the forecast SA2s, Balga – Mirrabooka had relatively poor access to podiatry services and a relatively high population of individuals admitted with a high risk foot diagnosis. This area can be considered a potential priority area for a place-based podiatry service intervention.

Figure 23 Location and FTE of podiatry clinical services in metropolitan region, 2017

Legend

Clinical service FTE0.60.7 - 1.31.4 - 2.32.4 - 3.0

3.1 - 4.9

SLA Osborne Park Hospital Royal Perth

Hospital St. John of God Midland

Sir Charles Gairdner Hospital

Bentley Health Service

Fremantle Hospital

Fiona Stanley Hospital

State rehab (FSH)

Armadale Hospital

Kwinana Community Health Development Centre

Rockingham - Kwinana District Hospital

Mandurah Community Health and Development Centre

Ü 0 510 20

km

Figure 24 Mean annual population of individuals admitted with high risk foot diagnoses between 2011/12 and 2015/16, by SA2 region

Ü0 5 10 20 30

km 51.9 - 86.2

(! !

(

(!!

(!(!( !

(!( !

(!( !

(

!(

!(

!( Legend

!( Podiatry clinical services No pop.

Mean admitted pop.0.2 - 16.616.7 - 27.227.3 - 38.638.7 - 51.8

Figure 25 Mean population-weighted percentile index of accessibility to podiatry services between 2011/12 and 2015/16, by SA2 region.

Ü0 5 10 20 30

km 81 - 100Legend

Podiatry clinical services No pop.

Mean accessibility index (pct.)1 - 2021 - 4041 - 6061 - 80

Figure 26 Mean percentile index of accessibility to podiatry services relative to the mean annual population of residents admitted with high risk foot conditions. Each point represents an SA2 region in metropolitan Perth.

908070605040

Belmont-Ascot-Redcliffe60

Lockridge- Kiara

3020100

Midland -Calista

Good access / Low population

Mean accessibility index (pct.)

100

Cannington-Queens Park

Good access / High population

Bentley-Wilson-St James

Parmelia-Orelia

Willagee80

Maylands

Rivervale-Kewdale-Armadale-Wungong- Cloverdale

Brookdale

Nollamara-Westminster

Mean admitted population

40

Girrawheen

Balga -

20

Poor access / Low population

0 Poor access / High population


Discussion

High risk foot admissions represent a diverse range of conditions and degrees of clinical severity. Different diagnosis/procedure sub-groups within high risk foot admissions have different patterns of expected hospital demand. It is important to examine differential geographical and demographic admission rates between high risk foot diagnosis/procedure sub-groups. Knowledge of such differences can help profile individuals likely to be admitted with high risk foot conditions in different regions and at different clinical sites. This data can have implications for health service delivery and planning.

A recent study suggested that costs over five years could be reduced by up to $10,000 if foot ulcer patients had treatment according to recommended guidelines as op- posed to usual care (Cheng, 2016). The data presented here indicate that this would represent a 20% reduction in hospital costs over five years for the average high risk foot patient. However, interventions are likely to differ in effectiveness between different sub-groups of high risk foot admissions.

Among 14 metropolitan SA2s forecast to have a disproportionately high rate of high risk foot admissions among residents up to 5 years into the future, Balga – Mirrabooka is identified as a potential priority area for a place-based podiatry services intervention: it had a relatively poor index of accessibility to podiatry services combined with a relatively high population of individuals admitted with high risk foot conditions.

Finally, it is proposed that counterfactual analysis is done using historical occasion-of-service podiatry data from each clinic. Such analysis would determine the impact of podiatry service delivery on patient outcomes and state- wide hospital costs. Results from the counterfactual analysis of such podiatry service data, when linked to existing hospital records from the WA Data Collections, would be invaluable in the decision to allocate podiatry resources to populations most in need.

Caveats

This report has a number of caveats which should be considered in interpreting our findings. First, this report has relied upon hospital admissions for high risk foot and its components as indicators of need. However, admissions are the result of realised accessibility of hospital services. Realised accessibility of health services is influenced not only by true health needs, but also health needs as per- ceived by the individual, and factors such as the avail- ability, accessibility, and approachability of hospital and non-hospital services (Levesque, 2013).

Secondly, our analysis has used aggregate data over large populations. Therefore, no conclusions may be drawn regarding individuals or important sub-groups within the groups described here. Just as important differences can be identified between diagnosis and procedure groups within high risk foot, differences in hospital use are likely to be observed according to various age groups, Aborigi- nal status, and rurality.

ReferencesAIHW. Health expenditure Australia 2013-14. Australian

Institute of Health and Welfare; 2015. 54.

Lazzarini PA, Hurn SE, Kuys SS, et al. The silent overall burden of foot disease in a representative hospi- talised population. International Wound Journal; 2016.

Stephen Duckett KG. Perils of Place: Identifying hotspots of health inequality. Grattan Institute; 2016.

Levesque J-F, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Inter- national Journal for Equity in Health; 2013.

Cheng Q, Lazzarini PA, Gibb M, et al. A cost-effectiveness analysis of optimal care for diabetic foot ulcers in Australia. International Wound Journal; 2016.

Tuson, M., et al. “Forecasting hotspots of potentially preventable hospitalisations with spatially aggregated longitudinal health data: all subset model selection with a novel implementation of repeated k-fold cross-validation.” Manuscript submitted for publication. 2017.

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Documents

HRF Report V6 (jpeg v).indd · Web viewHigh risk foot admissions comprise five different diag- nostic and procedural sub-groups; cellulitis of the lower limb, peripheral vascular