stephanie knox (now at chere) helena britt christopher harrison

A collaborating unit of the Australian Institute of Health and Welfare and the University of Sydney

BEACH A BEACH SAND study

Estimated prevalence of chronic illnesses (identified as Health Priority Areas)

among general practice patients

Stephanie Knox (now at CHERE)

Helena BrittChristopher Harrison

Australian GP Statistics & Classification Centre A collaborating Unit of the Australian Institute of Health and Welfare

& the University of Sydney

Estimated prevalence of NHPA morbidities. SPH Seminar August 2007

BEACH Today’s aims

About us

BEACH methods

Representativeness

Some changes in morbidity managed by GPs

SAND sub-studies – methods

Results of the prevalance study


BEACH The Family Medicine Research Centre

A University of Sydney Research Centre academically attached to the Department of General Practice & School of Public Health

A Collaborating Centre of the World Organization of Family Doctors (Wonca)

All moneys are self-generated through competitive grants, consultancies and contracts.

Our work is health services research and development.

The Australian GP Statistics & Classification Centre

A collaborating unit of the Australian Institute of Health and Welfare and the University of Sydney, within the FMRC.

Responsible for the BEACH program which is conducted under the AIHW Act, with Ethics approval from the University and AIHW Ethics Committees


BEACH BEACH

Began April 1, 1998Now in its 10th year (April 1, 2007)~9,000 participants

Representing 7,500+ GPs >40% of recognised GPs)

Data available ~ 900,000 encountersData being used by the profession,

researchers, governments & industry


BEACH

BEACH Aims

to provide a reliable and valid continuous national data source of timely GP–patient encounter information

to inform the community of the care provided to the vast majority of the population by GPs - the gate keepers of our medical system

to assess patient health risk factors, prevalence of disease, or longer term management--on selected subjects in sub-samples of patients- up to 20 sub-studies per year (SAND- Supplementary Analysis of Nominated Data)


BEACH BEACH methods

A cross sectional encounter based studyPaper based data collectionNational GP random sample (drawn by DoHA)1,000 GPs per year 20 per week x 50 weeks a year - ever changing100 consecutive encounters per GP All types of encounters includedNational data for 100,000 encounters p.a.


BEACH

Post-stratification weights

To ensure national representation of GP encounters, each year the data are weighted

1. To correct for the under-representation of young GPs <35 years (small but important)

2. To give more weighting to the encounters of busy GPs and less to (e.g.) part-time GPs data are weighted by HIC A1 Medicare claims previous quarter.


BEACH Observed vs expected age distribution

by state

NSW

0

5

10

15

20

25

30

0-4 5-14

15-24

25-44

45-64

65-74

75+

% e

nc

s Expectedencounters

observed

Expected = MBS (A1) claims, observed = BEACH sample



by state

NT

0

5

10

15

20

25

30

35

40

0-4 5-14 15-24 25-44 45-64 65-74 75+

age group

Expectedencounters

observed



by state

WA

0

5

10

15

20

25

30

0-4 5-14 15-24 25-44 45-64 65-74 75+

Age gp

% e

ncs

Expected encounters

observed


BEACH

BEACH variables (see recording form)

G.P characteristics (GP completed questionnaire)

Practice characteristics (GP completed questionnaire)

Patient characteristics Encounter details Patient reasons for encounter (up to 3) Problems managed (up to 4)


BEACH

BEACH variables Management (of each problem)

Medications prescribed, supplied, advised (up to 4/problem)

Non-pharmacological treatments (up to 2/problem)

•Clinical treatments

•Therapeutic procedures New referrals & admissions (up to 2 – linked to problem(s))

Pathology tests ordered (up to 5–each linked to problem(s))

Imaging & other tests ordered (up to 2 - linked)

(view form)


BEACH

G.P Characteristics age sex country of graduation years in general practice FRACGP currently in training program practice location (State,RRMA, ARIA, SEIFA) practice size use of computers

+ other variables over the years


BEACH

Patient characteristics

Age (from DoB) Sex Status to the practice (new/seen before) NESB (Yes/no) Aboriginal (self ID – Yes/no) Torres Straight Islander (self ID – Yes/no) Health care card holder (Yes/no) VA card holder (yes/no) Reasons for encounter (up to 3)


BEACH Some changes over time

A quick look at: patients seen

problems managed


BEACH Age of patients at encounter

changing over time (% of workload)

0

5

10

15

20

25

30

<15 yrs* 15-24 yrs 25-44* 45-64 yrs* 65-74 75 + *

1990-911998-992005-06


BEACH

Summary of problems over time

0

20

40

60

80

100

120

140

160

RFEs Problemsmanaged

New problems Chronicproblems*

1990-911998-992005-06


BEACH Selected morbidity management rates over

time - rate per 100 encounters

0

1

2

3

4

5

61990–91

1998–99

2000–01

2005–06


BEACH Other problems managed over time rate per

100 encounters

0123456789

101990-91

1998-99

2005-06


BEACH

Encounter data

Tells us about: patient demand for care the problems managed How specific problems are managed by GPs

It does NOT provide prevalence estimates of disease: among the attending population

(attendance gives chance of selection) among the population at large


BEACH SAND:

Supplementary Analysis of Nominated Data

““The bit on the bottom of the form”

Questions usually asked of the patient

Each GP pack divided into three: 40 + 30 + 30

• 40 BMI, smoking status, alcohol intake

• 30 nominated subject 1

• 30 nominated subject 2

Nominated subject 5 weeks -- 3000 random sample


BEACH

Available throughhttp://www.fmrc.org.au

(go to ‘BEACH’ and select ‘publications’)

Also see ‘Abstracts’ in the publication section of our

website

Hard copies of reports fromhttp://www.aihw.gov.au(cost $15-27 each book in GEP

series)

AGPSCCPhone +61 2 9845 8151

email: [email protected]


BEACH Population prevalence of disease

Important for health policy and health service planning

In Australia usually rely on results from the National Health Survey (NHS) (conducted by the ABS)

NHS estimates population prevalence based on patient self-reported morbidity

Using structured interview, with trained interviewer, in respondent’s home to elicit health-related information


BEACH National Health Survey

Advantages: includes a representative sample of the total

population (n = about 27,000 in 2004)Shortcomings:

Only conducted every 3 years ( 4 in the past) Self-report likely to be unreliable (lack of

clinical knowledge, recall issues etc)


BEACH The international scene

Most countries rely on similar national surveys Many people question the reliability of such estimates Some studies suggest patient recall of morbidity better

for well defined conditions (e.g. Diabetes, heart disease) than for ill-defined conditions (e.g. back pain)

Accuracy of household health data tested by Moore (US) in 1972, on chronic disease prevalence One third reported 1+ chronic disease (n=333) 106 medical records checked False positive chronic disease identification by patient

self report = 25%; false negative 38%


BEACH Prevalence data from EHRs?

Some have tried to produce prevalence estimates from GP EHRs

This works better in countries with patient registers than in fee for service system.

Major issue is incompleteness of recordSome question why we need to sample

people who do not utilise the health care system (i.e. currently not a cost)


BEACH Our study

Aims:

to use an ongoing national survey of GP activity to provide estimates of the prevalence of diagnosed morbidity among the population of general practice patients in Australia.


BEACH Methods

A SAND (Supplementary Analysis of Nominated data) substudy in the BEACH program

‘the bit on the bottom of the form’

BEACH GPs 12/07/05-19/08/05 & 25/10/05-28/11/05

Each GP asked, for 30 consecutive patients‘Does this patient have any of the following conditions which require ongoing management?’

Final sample 9156 from 305 GPs


BEACH Conditions listed on the form

Tick boxes provided, multiple response allowed

Cardiovascular disease--Ischaemic heart disease--Cerebrovascular disease--Peripheral vascular disease--Congestive heart failure--Hypertension – complicated--Hypertension – uncomplicated--Other

Arthritis--Osteoarthritis--Rheumatoid arthritis--Other arthritis

Diabetes--Type 1--Type 2--Other

Psychological problems--Depression--Anxiety--Insomnia--Other psychological problem

Other problems--Hyperlipidaemia--Chronic back pain--Malignant neoplasms--Gastro-oesophageal reflux disease

Respiratory problems--Asthma

-- mild --moderate--severe

--Chronic obstructive airways disease None of these conditions.


BEACH Prevalence among whom?

Patient sample drawn at point of GP-patient encounters is biased towards frequent attenders, who are older and have more health problems than population as a whole.

Sample will over-estimate prevalence of health conditions in the total patient population


BEACH Adjust to patient population

1. Enumerate the proportion of the sampled general practice patients who have a chronic condition requiring ongoing management

2. Use these crude sample rates to estimate prevalence in the population of patients attending GPs at least once, by adjusting for annual visit rates.


BEACH Methods (cont.)

Crude rates calculated (% with the disease)Crude rate weighted by visit frequency

related to age and sex, by weighting SAND sample against age-sex distribution of population who visited a GP (1+ times) in 12 months (April 2004-March 2005)

Used SAS V9.13 to adjust for the cluster design of the study.


BEACH The underlying theory

Recording of morbidity present in the patient will be more reliable with the combined input of: the patient, the GP, and the medical record (where available)

than patient self-report alone.


BEACH Results – Age-sex distribution c/f

population attending GPN = 9,156 patients from 305 participating GPs.

0

5

10

15

20

25

30

35

40

45

50

%

Male 0-14 yrs 15-24 yrs 25-44 yrs 45-64 yrs 65-74 yrs 75+ yrs

National GP patient population

SAND sample


BEACH Prevalence – crude rates

(what’s in the waiting room)

0

5

10

15

20

25

30

35

40

None of listed

1+ CardioVD

1+ Psych probl'm

1+ Arthritis

Hyperlipid

GORDAsthma

1+ DiabetesCOAD

Malignancy



0

5

10

15

20

25

Hypertension

Uncomplic Hypert

Complic Hypert IHDCerebVD

Per cent of attending patients


BEACH

0

5

10

15

20

25

DepressionAnxiety

InsomniaOther


Prevalence – crude rates



0

5

10

15

20

25

IDDMNIDDM OA

Rheumatoid

Chronic back pain




0

5

10

15

20

25

Asthma Mild

ModerateSevere



BEACH Prevalence – crude rates + adjusted

0

5

10

15

20

25

30Per cent of attending patients

Per cent of patient population



0

5

10

15

20

25

Psych probl'm

DepressionAnxiety

InsomniaOther





0

5

10

15

20

25 Per cent of attending patients




0

5

10

15

20

25

GORDAsthma

Mild

ModerateSevere

COAD

Malignancy




BEACH Est prevalence in GP patient pop’n c.f.

population prevalence (NHS 2004)

0

5

10

15

20

25

Hypertension IHD

CerebVD

Hyperlipid

IDDMNIDDM

Prevalence in patient population

Population prevalence NHS




0

5

10

15

20

25

Any psych probl'mDepression

Anxiety

Patient populationNHS




0

5

10

15

20Patient population

NHS


BEACH What about population prevalence?

If we assume that all of the 15% who did not attend a GP in 2005 have none of the listed diseases under management,we have to add these into the denominator, to gain an estimate of prevalence among the total population


BEACH

Prevalence in patient population –extrapolated to total population c.f. NHS

0

5

10

15

20Extrapolated patient prevalence

NHS 2004


BEACH Discussion

Differences in question SAND ‘ongoing management’ vs NHS present/absent SAND Chronic back pain vs back pain

Methods seem appropriate – supported by decrease population estimate for prevalence of Asthma to LESS than the NHS – this suggests that the assumption of no attendance = no disease does not apply to asthma.

Certainly the costs of collection are marginal c.f. NHS


BEACH Conclusion

At marginal cost to the BEACH program we can gain reliable estimates of the prevalence of diseases classed as National Health Priority Areas, among the population who attend a general practitioner in Australia.

These estimates would assist in health care planning by allowing estimates of future health service needs at a national and local level.

Extrapolation to the total population may raise methodological questions, but the results may well be more reliable that the NHS.


BEACH

BEACH BEACH 06-0706-07

Many thanksMany thanksto the GPsto the GPs


BEACH

Beach 05-06


BEACH

Available throughhttp://www.fmrc.org.au

(go to ‘BEACH’ and select ‘publications’)

Also see ‘Abstracts’ in the publication section of our

website

Hard copies of reports fromhttp://www.aihw.gov.au(cost $15-27 each book in GEP

series)

AGPSCCPhone +61 2 9845 8151

email: [email protected]

stephanie knox (now at chere) helena britt christopher harrison

Documents