presentación de powerpoint - oecdcsv 0.2 included red dots represents the existence of neonatal icu...
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ATLAS VPM
the Healthcare Quality of the SNHS under scrutiny
Enrique Bernal-Delgado
www.atlasvpm.org
Who we are and what we do
• Collaborative health services research project which aims to describe
systematic and unwarranted variations in medical practice and
healthcare outcomes, using a population-based and a hospital-
specific approach.
• … providing insight (i.e. underlying factors analysis) for decision-
makers to make better decisions; and yielding relevant information for
hospital managers to look at those underperforming quality areas.
• … using and developing reliable methodologies
• … using several strategies for translating knowledge into practice
Does the place of
residency influence
the population
exposure to effective
and safe care?
Geographical-based
approach
Is the likelihood of
getting high-quality
and safe care
dependant on the
provider where a
person is assisted?
Hospital-specific
approach
DWH: logic structure
Getting rid of “bad” variation
• Proper identification of unwarranted and systematic variation
– How large variations are? – How to get rid of randomness and over-dispersion? – How to account for need (burden of disease)? – How to flag areas beyond the expected?
• Exploring underlying factors: pursuing proper attribution – Supply effect – Socioeconomic gradient – Variation over time – Population flows influence – Adequate unit of analysis
Unwarranted and systematic variation Some examples from the Atlas in Spain
Cardiovascular ischemic disease: PTCA
EQ 5.1
EB 0.34
PTCA Standardized rates (10,000 inh.) Standardized Utilization Ratio
Cancer
EQ 11
EB 0.9
Prostatectomy in prostate cancer Standardized rates (10,000 inh.)
Conservative mastectomy in breast cancer Standardized utilization ratio
Obstetric care: c-section
EQ 3
CSV 0.2
Red dots represents the existence of neonatal ICU
3.4 4.1 4.9 13.5
All risks
included
No risks
included
C- section at population-level Standardized rates (10,000 inh.)
C- section at hospital-level Adjusted risk (100 deliveries)
Avoidable hospitalizations
CHF DIAB DH COPD ANG AST ALL
RV 4.1 4.5 2.6 4.5 12 9.4 3.1
EB 0.12 0.2 0.1 0.2 0.5 0.5 0.11
Low value care
procedures with a more effective or cost-effective alternative
Proctologic
Surgery
Knee&Hip
Revision
Spinal
Fusion Tonsilectomy
RV 3.10 4.64 5.78 4.19
EB 0.12 0.20 0.26 0.20
Getting rid of “bad” variation
• Proper identification of unwarranted and systematic variation
– How large variations are? – How to get rid of randomness and over-dispersion? – How to account for need (burden of disease)? – How to flag areas beyond the expected?
• Exploring underlying factors: pursuing proper attribution – Supply effect – Socioeconomic gradient – Variation over time – Population flows influence – Adequate unit of analysis
Ecologic associations: a step forward
Regional policies
Social gradient Capacity
Learning cascades
PTCA vs Burden of ischemic disease
Population differences (need) must be controlled, to elicit unwarranted variation.
Basic standardization, age and sex, is not always enough.
Gender analyses: Congestive hearth failure
Although needed, standardization might conceal differences across age-groups
or between genders
Gender analyses: discrepant pattern
Brownish areas represent areas were women experience proportionally more CHF
avoidable hospitalizations as compared to men.
Cross-section designs have some limitations: Variation varies over time differently across areas. Improvements in area B would have remained unnoticed.
PTA Sd rates
2002-2009
A
B
B
A
A quite high ICC together with quite important differences at very small level, where GPs are expected to act as perfect “agents”.
Region ICC: 0.4
Unit of analysis should represent the context where the levers of variation act.
But, at the same time, cluster effects are common and deserve further analyses.
A single map sometimes is not enough. First map represents the portion of variation
attributable to unobserved factors affecting each area as if areas were independent
from each other. Second map represents the variation related to vicinity effects.
Area level effect plus vicinity effects: PTA variation across areas
Getting rid of “bad” variation
• Proper identification of unwarranted and
systematic variation
• Exploring underlying factors: pursuing proper attribution
ATLAS VPM
the Healthcare Quality of the SNHS under scrutiny
Enrique Bernal-Delgado
www.atlasvpm.org