Far North Queensland A new Centre for Chronic Disease Prevention at JCU Cairns Snapshot of past current and future work Robyn McDermott MBBS FAFPHM MPH PhD Director CCDP JCU Cairns ID: 760355
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Slide1
The rise and rise of chronic disease in Far North Queensland A new Centre for Chronic Disease Prevention at JCU Cairns Snapshot of past, current and future work
Robyn McDermott MBBS, FAFPHM, MPH, PhD. Director CCDP, JCU CairnsCBH Grand Rounds Friday 28 March 2014. Block “A” Lecture Theatre 12.15-1.30pm
Slide2Today
Brief background and selected past and current descriptive work in far north Queensland
A
pproach of the CCDP
I
nterventions
Where are we heading?
Slide3Some “political arithmetic of crowd disease” in Australia:CVD Death rates, 2007-8Source: AIHW 2011, Age-standardised deaths per 100,000
Slide4CVD hospitalisation rates, 2007-8Source AIHW 2011: Age standardised hospitalisations per 100,000
Slide5Prevalence of diabetes, Indigenous NQ (WPHC) and Australia (AusDiab), 1999-2000
Slide6Age standardised rates for “ACS” avoidable admissions by Queensland Health District, 2003-6Source: QHAPDC, 2007 (rates per 100,000)
Slide7Ambulatory Care Sensitive (ACS) avoidable hospitalisations for selected chronic diseases, Queensland, 1999-2006.Source: QAPDC, 2007, rates per 100,000
Slide8Potentially Preventable Hospitalisations in SA (2007-9) - Top 15
Slide9Adjusted incidence rate ratios for CHD events in FNQ Aboriginal and TSI adults, 2000-7 (n=1706)Source: McDermott et al, MJA, 2011
Measure
IRR
95% CI
Obesity
1.7
1.01-2.8
High BP (>140/90)
1.5
1.01-2.3
Smoking
1.4
0.9-2.2
Low HDL (<1.0mmol/l)
1.3
0.9-1.9
High TG (>=2.0 mmol/l)
1.9
1.3-2.7
IFG
(FBG 5.5-6.9 mmol/l)
1.3
0.8-2.2
Diabetes
(FBG >=7.0)
2.4
1.6-3.6
Micro-albuminuria
1.4
0.9-2.3
Macro-albuminuria
4.6
2.9-7.1
Slide10Glycemia and albuminuria, especially when combined, predict much of the “gap” in CHD incidence
Baseline prevalence of high glycemia is >25%
Baseline prevalence of albuminuria (>3.4 mmol/l) = 33.5%
Those with diabetes at baseline were 5.5 (4.2-7.3) times more likely to have albuminuria than those without diabetes
Adjusted CHD IRR for both diabetes and albuminuria = 5.9 (3.4-10.1)
Slide11Risk accumulation along the care continuum
Low birth weightMaternal diabetes in pregnancyEpigeneticsAdolescent adiposityPoor nutritionSmokingHigh BPLipidsGlycaemiaetc
Screening and Secondary prevention in primary care
Hospitalisation
for complications
Death
Rehab
“pushback” – CCDP preventive approach
Slide12Improve preventive systems for CD management
Slide13Cluster Randomised Trial of HW-managed diabetes care system improvement in the Torres Strait, 1999-2001.
Slide14Slide15Hospitalisation of people with diabetes, Torres Strait, 1999-2002 (n=921), Cape York 2002-3 (n=240): Proportion of diabetics hospitalised for avoidable conditions in previous 12 months
Slide16Measure2004, n=342009, n=67ANDIAB 2009Age5452.456.8Median HbA1c9.359.538.0Current smokers (%)29%30%10%% “good” glycemic control (A1c<7%)162026% taking insulin16%32%35%% without albuminuria25%33%67%Mean weight, kg (BMI)96.14 (34.7)101.74 (35.9)N/A (30.2)
Can improved care processes be sustained with rising caseloads and current workforce configuration?
Snapshot from Island in the Central
Group Torres,
2009.
Incident cases 3%, younger ages, increasing obesity
Source: Forbes et al, 2012.
Slide17Getting Better at Chronic Care (GBACC) in North Queensland: a cluster RCT of community health worker care co-ordination in remote FNQ settingsRobyn McDermott, Barbara Schmidt, Vickie Owens, Cilla Preece, Sean Taylor, Adrian Esterman
Slide18“Getting better at chronic care”Cluster RCT of health-worker led case management for high risk clients
Aim:
Test if HW-led care for high risk poorly managed adults with complicated T2DM would improve care processes (checks, referrals, self management) and outcomes
Primary outcome: improved HbA1c
Secondary outcomes: Improved
QoL
, reduced CVD risk factors and complications (avoidable
hospitalisations
)
Mixed methods evaluation in 3 phases
NHMRC Partnership Project, 2011-2015
Slide19GBACC: mixed methods evaluation in 3 phases
Slide2012 Participating Communities*Intervention sites in phase 1 (randomly allocated)
Torres and NPA HHSBadu*BamagaInjinoo*New MapoonSeisiaUmagico*
Cape York HHSKowanyama*Mapoon*Mareeba (Mulungu)Cairns and Hinterland HHSMossman Gorge (ACYHC)*NapranumYarrabah (GYHS)
Slide21PHASE 1: COCONSORT DIAGRAM: GBACC, 2012-14, 2012-14RCT)
Enrolment: 12 sites recruited and 327 patients assessed as eligible
Excluded: 114 patients declined to participate
Group randomisation: 12 sites
Allocation
Intervention: 6 sites (n=100 patients)Received intervention, n=100
Allocated to waitlist group: 6 sites(n=113 patients)
Follow up
Lost to follow-up (n=16)Moved away (12)Died (4)
Lost to follow up (n=6)Moved away (3)Died (2)Withdrew from study (1)
Analysis
Analysed for primary outcome, n=108 (96%)
Analysed for primary outcome, n= 84 (84%)
Baseline data
collected, n=213
Slide22Clinical care processes at baseline and follow up (%)
Baseline
Endpoint (excluding 22 loss of follow up)
Control n=113
Intervention n=100
Control n=107
intervention n=84
No
% (95% CI)
No
% (95% CI)
No
% (95% CI)
No
% (95% CI)
Foot check%
50
44.2 (35.0-53.5)
31
31.0 (21.8-40.2)
38
35.5 (26.3-44.7)
26
31.0 (20.9-41.0)
Seen by DM educator %
46
40.7 (31.6-49.9)
52
52.0 (42.1-61.9)
41
38.3 (29.0-47.6)
44
52.4 (41.6-63.2)
Seen by dietician %
22
19.5 (12.1-26.8)
30
30.0 (20.9-39.1)
21
19.6 (12.0-27.2)
37
44.0 (33.3-54.8)
Dentist check %
20
17.7 (10.6-24.8)
13
13.0 (6.3-19.7)
9
8.4 (3.1-13.7)
15
17.9 (9.6-26.5)
ECG check%
37
32.7 (24.0-41.5)
42
42.0 (32.2-51.8)
34
43.9 (34.4-53.4)
35
40.5 (29.8-51.1)
Eye check %
54
47.8 (38.5-57.1)
42
42.0 (32.2-51.8)
56
52.3 (42.8-61.9)
37
44.0 (33.3-54.8)
Smoker %
38
34.5 (25.6-43.5)
34
35.1 (25.5-44.7)
33
31.2 (22.4-40.4)
34
41.5 (30.7-52.2)
Blood sugar self-monitor %
45
40.9 (31.6-50.2)
46
46.0 (36.1-55.9)
63
59.4 (50.0-68.9)
44
52.4 (41.6-63.2)
Taking insulin%
55
48.7 (39.4-58.0)
40
40.0 (30.3-49.7)
47
43.9 (34.4-53.4)
40
47.6 (36.8-58.4)
Dyslipidemia %
83
73.5 (65.2-81.7)
84
84.0 (76.7-91.3)
91
85.0 (78.2-91.9)
76
90.5 (84.1-96.8)
Taking lipid lowering medicines%
5
4.4 (0.6-8.3)
3
3.0 (-0.4-6.4)
3
2.8 (-0.4-6.0)
5
6.0 (0.8-11.1)
Albuminuria and taking ACEi or ARB drugs
46
88.5 (79.6-97.3)
47
88.7 (80.0-97.4)
58
82.9 (73.9-91.8)
51
89.5 (81.4-97.6)
Adherent to all medicines
53
46.9 (37.6-56.2)
55
55.0 (45.1-64.9)
57
53.3 (43.7-62.8)
41
48.8 (38.0-59.6)
Had Fluvax
50
44.2 (35.0-53.5)
66
66.0 (56.6-75.4)
51
47.7 (38.1-57.2)
50
59.5 (48.9-70.2)
Slide23HbA1c measures at baseline and follow-up by group, absolute values: GBACC Phase 1 trial results
Slide24FNQ Hospital Avoidance TrialCairns, Innisfail, Mareeba2014-16
Health Innovation Fund Project OverviewFunded by QH (CARU)Neil Beaton, Mary Streatfield, Robyn McDermott
Slide25Aim: to evaluate a new approach to community-based management of “frequent flyers” in FNQ hospitals –Hospital Avoidance Trial, 2013-16
Background: Pilot HAP in Cairns showed a dramatic reduction in ED and inpatient episodes in 68 frequent flyers using a nurse-led case management approach
.
Pragmatic RCT of intensive community-based case management of frequently
hospitalised
adults with chronic conditions in 3 CHHHS sites
530 patients in 3 sites randomly assigned to
265 Intervention: usual care plus shared
electronic record including CDM
tool, close case management (caseload for each care co-
ord
=<40) and self-management training and support
265 “controls”: usual care (referral to a medical home with offer of shared record
)
Eligibility criteria: 8 or more ED/inpatient episodes in the previous 12 months
Evaluation endpoints: Avoidable ED visits or hospital admissions over 18 months, care processes (GPMP, referrals, self management training), intermediate clinical indicators (HbA1c, BP, Lipids, UACR/
eGFR
), disease progression, quality of life
Economic (DRGs and
AQoL
) and process evaluation
Slide262012-13 FY ED and Separations (patients)
Number of Visits
>=5
>=8
Cairns
ED
1,105
324
Inpatient
543
187
Total
2,979
1,006
Mareeba
ED
751
235
Inpatient
122
40
Total
1,077
352
Innisfail
ED
369
104
Inpatient
95
32
Total
682
234
Total of three sites
ED
2,225
663
Inpatient
760
259
Total
4,738
1,592
Slide27FNQ HAT Trial design
Patient recruitment 3 sites, n=530Baseline interviews + data collection
Randomisation
Control group: n=265
Usual best practice careGPMP, cdmNet audit & feedback
Intervention group: n=265GPMP, cdm tool audit & feedback+ Case manager
Follow up data collection:
Interviews, ED & inpatient episodes
Cdm tool audit, HIC/PBS, costings
Follow up data collection:
Interviews, ED & inpatient episodesCdm tool audit, HIC/PBS, costings
Process evaluation including fidelity of implementation
Slide28The patient journey, FNQ HAT
Patient identified as eligible by EDIS/HBCISand invited to participate in the trial
Consent obtained
Consent not obtained
Not in trial, usual care
Care co-ordinator conducts baseline assessment and interview, arranges GP referral and GP consent to be in trial
Randomisation
Intervention group:
GPMP, referrals, CDM tool, Care co-ordination, self management training and support
Usual care group:
Offer of shared record, Referrals to AHPs
GPMP and referrals, care
co-ordinator
Self management training
Allied health and medical specialists
Other services as required
Data capture and QI reports to GPs from ED/IP and CDM tool
Hospital admissions and ED visits
Slide29Why a Randomised Controlled Trial Design?
RCT is the most robust study design which will give the highest level of evidence: all previous published studies looking at hospital avoidance (a complex intervention in a complex environment) were uncontrolled before-and-after designs – weak evidence for policy change and unable to be properly evaluated economically
Controls provide the counterfactual for robust clinical and economic analysis
Randomisation
deals with selection/allocation bias
Controls deal with secular trends in exposures and outcomes, regression to the mean and changes in the policy and fiscal environment.
Good pilot data gives a clear effect size so a robust power calculation (sample size) will ensure the question can be clearly answered without (too much) statistical error
Will be publishable and in the public domain, not sit on the shelf
High scientific quality will be competitive for matching NHMRC Partnership Project Grant funding
Slide30Expanding the impact of our researchSource: Duryea, Hochman, Parfitt. Research Global: Feb 2007.
Research outputs:egDiscoveriesPublicationsPatents
ResearchTransfer: Engagementwith endusers
ResearchOutcomes:New products or services
Research Impact:Valueadded,Improvements achieved
National benefits
Traditional quality domain
Research impact scope
Slide31Association between PHC resourcing (staff) and costs of hospitalisation among diabetics in FNQ remote communities, 2001-5 (Gibson, Segal, McDermott 2011)
Slide32ACKNOWLEDGEMENTS
The CCDP is supported by QH Senior Clinical Research Fellowship and the Australian Primary Health Care Research Institute (APHCRI) as a PHC Centre for Research Excellence (CRE)GBACC is supported by NHMRC Partnership project grant 570149 FNQ HAT is funded by QH (CARU)CCDP and CRE team includes: Admin: Jacqui Lavis and Sally McDonald Clinical Epidemiology: Sandy Campbell*, Robyn McDermott*, Klaus Gebel, Linton HarrissBiostatistics/informatics: Haider Mannan, Arindam DeyCommunity-based prevention studies group: Alan Clough*, Caryn West*PhD students: Ashleigh Sushames, Sean Taylor*, Barb Schmidt, Jan Robertson, Dympna Leonard, Russell Hayes, Richard Turner, Malcolm Forbes* (Masters)Health Economics: Kenny LawsonClinical Research Associates: Vickie Owens, Cilla PreeceCollaborating institutions: QH, UniSA, SAHMRI, UQ, Melbourne University, Baker-IDI, Menzies School of Health Research, Apunipima CYHC, Gurinny, Mulungu, AHCSA, QAIHC, UNSW*Receiving NHMRC or NHF Fellowship support