QUALITY VARIATIONS: AN EXPLORATIVE STUDY TO ASSESS THE LINK BETWEEN PRIMARY CARE QUALITY AND PRIMARY CARE SYSTEM CHARACTERISTICS

European Forum for Primary CareGraz, 16 September 2011

Gerrard Abi-AadOECD Policy Analyst (Employment, Labour and Social Affairs Division)

Agenda

1. Primary care in health systems and epidemiological transition

2. Background/justification of the work3. Data4. Methodology5. Preliminary results6. Conclusions7. Next steps

1.Primary care in health systems and epidemiological transition

Strong evidence supporting the important role of primary care in preventing illness and promoting health and, in turn, reducing the need for costly hospital care and potentially the number of premature deaths.

However, little attention is paid to the study of the development of primary care systems and in particular to the known attributes through which primary care mediates its benefits.

1.Primary care in health systems and epidemiological transition

• In most developed countries, chronic conditions account for both the majority of deaths and the majority (up to 75%) of health care spending.

• The increase in chronic disease prevalence is accompanied by a steep increase in the prevalence of multi morbidity

• E.g. - a study based on data extracted from general practice registers in the Netherlands (Fortin, 2010) showed that the prevalence of multi-morbidity (patients with two or more co-existing conditions), ranged from around 17% in patients aged 20-39 to 77% patients aged 80 and over

• Chronic conditions cause most deaths and most health spending (up to 75%)

• Steep increase in multi morbidity: Patients with 2 or more conditions in the Netherlands– 17% of 20-39 age group– 77% of over 80s.

• Good primary care minimises acute exacerbation of chronic disease and reduce the volume of unplanned (expensive) avoidable admissions

• Patients often receiving treatment in multiple care settings – strong care coordination is more important than ever before

1.Primary care in health systems and epidemiological transition

Multi morbidity and its significanceCommonly occurring co-morbidity in a Scottish primary medical care population*.

Multimorbidity: impact on health systems and their development; Guthrie et al 2011: Commissioned paper by the OECD.

2. Background and justification of the work

• The proposal to carry out this work was presented and endorsed at a special meeting of the HCQI Expert Group on 7 October 2010.

• We will use this work to catalyse a new focus for the HPPP project including indicator development and utilisation of health system characteristic information.

• Finally, we see this work as a first step in our aim to better understand primary care quality variations and how they relate to the way in which primary care services are organised

3. Data

• Independent

1. Health system characteristics (Paris et al., 2010)

2. PHAMEU Monitor (NIVEL)

• Dependent

1. Potential years of life loss for conditions amenable to primary care management

2. Potential years of life loss for cancers amenable to early detection

3. Potentially preventable admissions

4. Methodological approach

Indirect measures of primary care quality (PYLL & PPA) to better

understand how quality varies

from country to country

1

Explore the link between primary care organization

and quality measures using multivariable

fractional polynomial regression

2

Use of cluster techniques to assess quality

patterns within and across

country clusters

3

5. Preliminary results

• High variation of quality of care:– General decrease in potential years of life loss in almost all

countries– BUT some significant outliers (amenable mortality)

Australia

Austria

Belgium

Canada

Czech Republic

Denmark

Estonia

Finland

France

Germany

Hungary

Iceland

Ireland

Israel

Italy

Japan

Korea

Luxembourg

Mexico

Netherlands

New Zealand

Norway

Poland

Portugal

Slovakia

Slovenia

SpainSweden

United Kingdom

United States

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

z-scores

5. Preliminary results

• Cancers amenable to early detection

5. Preliminary results

Cluster analysis

1. Level of copayment for primary care services.2. Density of family physicians per 100 000 population3. Total national expenditure on health as a proportion of GDP.4. Extent of GP gate keeping5. Ability to choose own GP6. Predominant practice structure (solo, mixed, multi)

5. Preliminary resultsCluster analysis

Organizational features of primary care are highly variable

But it is possible to group countries based on available system characteristic information

Cluster analysis (2)Ranking of clusters using quality scores may help in identifying quality trends?

Ranking Group A Group B Group C Group D Group E

Breast cancer 1 5 2 3 4

Cervical cancer 1 4 5 3 2

Prostate cancer 1 2 5 4 3

Skin cancer (M) 1 3 5 4 2

Skin cancer (F) 2 3 5 4 1

Colorectal cancer (M) 1 5 4 2 3

Colorectal cancer (F) 1 4 5 3 2

Amenable Mortality (M) 1 4 5 2 3

Amenable Mortality (F) 2 4 5 1 3

5. Preliminary results

5. Preliminary results

Regression modelling

– We fitted multivariable fractional polynomial (MFP) regression models. This type of model was deemed suitable because of the presence of a combination of continuous and categorical variables. MFP is also flexible in that it provides a range of options for model optimisation. All analyses were carried out using STATA (version 11.1)

– None of the fitted models had an adjusted R squared value above 0.5.

5. Preliminary results

Regression modelling

Density of GPs and total national expenditure on health seem to emerge as important ‘predictive’ variables in the model

Pol

Fin

Slo

NetNor

Srep

Ice

Swe

Est

Por

Hun

Ire

Cze

UK

Den

Ita

Lux

Spa

Fra

Ger

Bel

Aus

-2-1

01

2P

redic

tor+

resid

ual o

f am

_p_

z

0 50 100 150ACC1.1

Fractional Polynomial (3 3)

Est

Pol

Cze

Srep

IreLux

Hun

Slo

Fin

Spa

UK

Nor

Ita

IceSwe

Den

Net

Por

Bel

Aus

Ger

Fra

-2-1

01

23

Pre

dic

tor+

resid

ual o

f am

_p_

z

4 6 8 10 12tneh_N

Fractional Polynomial (2 2)

6. Conclusions

Lots of problems when trying to measure the ‘quality’ or ‘performance’ of a primary care system (missing data etc.).

• Modelling real causation (rather than correlation) is a difficult exercise and may not be feasible (or desirable)

• Real merit in the collection of system characteristic information (including PC funding and staffing structure)

• Making sense of common organizational features and grouping of countries (and in demonstrating the utility of system characteristic information) – may also have spin offs for relative comparative quality assessment (added value component)

• Added value in using additional quality of care measures (PYLL cancer and amenable mortality)

7. Next steps

1. To collect nationally verifiable primary care system characteristic information from all OECD countries..

2. To explore more fully the utility of statistical clustering and relative benchmarking in the context of health system comparative quality monitoring by using key primary care system attributes.

3. To explore the potential for an enhanced suite of primary care quality measures and in particular to focus the development of new measures encompassing; multi morbidity, cost effective prescribing and poly pharmacy, embedding mental health care in the primary care setting, a new suite PYLL indicators for amenable mortality and cancers amenable to early detection in primary care settings.

7. Acknowledgements

1. Y-Ling Chi2. Dione Kringos and Wienke Boerma and the NIVEL Institute3. Professor Bruce Guthrie

Contact

Gerrard.Abi-Aad@oecd.org