Composite quantitative analysis of international healthcare accreditation survey data

Australian Institute of Health Innovation

Composite quantitative analysis of international healthcare accreditation survey data

Max Moldovan, Charles Shaw and Jeffrey Braithwaite

Healthcare accreditation:

a process that a health care institution, provider, or program undergoes to demonstrate compliance with standards developed by an official agency.[Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health, Seventh Edition. © 2003 by Saunders, an imprint of Elsevier, Inc. All rights reserved.]

Study and data set description

The first international healthcare accreditation survey was undertaken in 2000, commissioned by the World Health Organisation [WHO Review 2003, WHO/EIP/OSD/2003.1]The current data set has been collected over 2009-201144 accreditation agencies, located in 38 countries10 broad categories of attributes (e.g. “Policy and governance” and “Funding of the organization”)173 questions leading to categorical, date/numeric or free text responses

Older than 20 years Between 10 and 20 y.o. <10 y.o.;

* If more than one program per country, the oldest program is displayed

Geographical and age distribution of accreditation agencies*

Methods of analysis

Stratified categorical analysis

Supervised learning via penalised regression

Unsupervised learning via hierarchical clustering


Organisations are stratified into two or more groups based on observed factorsResponses arranged into contingency tables Fisher’s exact conditional testing is appliedThe results are assessed based on the plots of sorted P-values


Stratification factors:

1.Purchasing Power Parity adjusted GDP per capita2.Government relatedness3.Regional factor4.Age of organisation5.Country of origin population


0.0

0.2

0.4

0.6

0.8

1.0

GDP PPP factor (US$20,000 split value)

Question IDs

P-v

alue

s

pol1

pol3

tra5

rep1

pol7

agh1

2re

p10

phc1

4fu

n1po

l16

phc1

2re

p4 int6

phc1

7fu

n5de

v8re

p13

int3

rep1

1fu

n4 tra1

phc8

agh8

pol1

9po

l10

rep2

pol6

agh1

4po

l13

rep1

2fu

n2re

p16

tra9

fun8

fun6

phc1

6po

l18

rep5 tra

6fu

n10

rep3 int1

pol1

7fu

n9re

p7re

p14

rep1

5ag

h16

agh1

7

0.05


R package penalized: Penalised regressions via L1and L2 penalties [Goeman (2010): Biometrical Journal 52 (1), 70–84]L1: LASSO; L2: ridge; L1 and L2: elastic netsOnly L1 penalty (LASSO) has been usedSupervisory (response) variable is an ordinal four-level categorical variable defined by field experts (similar to phenotypes defined by clinical experts)Explanatory variables are categorical responses coded as factorsLogistic link function has been used


LASSO [Tibshirani (1996): J. Royal. Statist. Soc. B 58 (1): 267–288] :


Vitality categorisation (“phenotype”):

0 – on “life support” and unlikely to survive1 – hardly functional and deteriorating2 – functional, with signs of improvement3 – alive and well


The set of shifted (“phenotype”) response variables:

yi,1 – refined “deprived”: 0 vs. 1-2-3

yi,2 – mid split: 0-1 vs. 2-3

yi,3 – refined “winners”: 0-1-2 vs. 3


4.0 3.5 3.0 2.5 2.0 1.5

-0.5

0.0

0.5

1.0

1.5

LASSO path

lambda1

coef

ficie

nt


0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

LASSO path: Entrance distances

lam

bda1

tra5a

rep1

dre

p11b

pol3

apo

l3c

pol1

6apo

l10b

pol1

cre

p14a

rep1

3gpo

l1a

rep5

cre

p13h

tra9e

rep1

0bpo

l18e

pol1

3bfu

n4c

fun4

bpo

l19c

fun1

fpo

l19b

pol3

apo

l16c

rep1

6apo

l19d

tra6a


0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


lam

bda1

tra5a

rep1

dre

p11b

pol3

apo

l3c

pol1

6apo

l10b

pol1

cre

p14a

rep1

3gpo

l1a

rep5

cre

p13h

tra9e

rep1

0bpo

l18e

pol1

3bfu

n4c

fun4

bpo

l19c

fun1

fpo

l19b

pol3

apo

l16c

rep1

6apo

l19d

tra6a


0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


lam

bda1

tra5a

rep1

dre

p11b

pol3

apo

l3c

pol1

6apo

l10b

pol1

cre

p14a

rep1

3gpo

l1a

rep5

cre

p13h

tra9e

rep1

0bpo

l18e

pol1

3bfu

n4c

fun4

bpo

l19c

fun1

fpo

l19b

pol3

apo

l16c

rep1

6apo

l19d

tra6a


“Winners” refined 50‐50 “Losers” refined

tra5a pol3a pol3a

rep1d pol13a pol10b

rep11b fun5b pol13a

pol3a dev8a rep11b

pol3c tra9b pol7a

pol16a rep11b fun1a

pol10b pol1a int6b

pol1c rep14b tra1c

rep14a tra5a fun10a

rep13g pol16a pol7d

pol1a fun4d pol19a

rep5c pol16d pol16d

rep13h fun1a rep3a

tra9e pol1c rep15a

rep10b fun8g tra1b

pol18e pol10b fun8g

pol13b pol6k fun4d



tra5a pol3a pol3a

rep1d pol13a pol10b

rep11b fun5b pol13a

pol3a dev8a rep11b

pol3c tra9b pol7a

pol16a rep11b fun1a

pol10b pol1a int6b

pol1c rep14b tra1c

rep14a tra5a fun10a

rep13g pol16a pol7d

pol1a fun4d pol19a

rep5c pol16d pol16d

rep13h fun1a rep3a

tra9e pol1c rep15a

rep10b fun8g tra1b

pol18e pol10b fun8g

pol13b pol6k fun4d



tra5a pol3a pol3a

rep1d pol13a pol10b

rep11b fun5b pol13a

pol3a dev8a rep11b

pol3c tra9b pol7a

pol16a rep11b fun1a

pol10b pol1a int6b

pol1c rep14b tra1c

rep14a tra5a fun10a

rep13g pol16a pol7d

pol1a fun4d pol19a

rep5c pol16d pol16d

rep13h fun1a rep3a

tra9e pol1c rep15a

rep10b fun8g tra1b

pol18e pol10b fun8g

pol13b pol6k fun4d


R function daisy (package cluster) has been used207 categorical attributes have been coded as asymmetric binomialsDissimilarity matrix has been computed based on Gower’s dissimilarity coefficient [Gower (1971) Biometrics 27,

857–874 ]Dendrograms are based on Ward’s hierarchical clustering methodWeights are imposed as an inverse of attribute counts within each category


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All categorical attributes


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ania

Can

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Lith

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All categorical attributes


Fran

ceSo

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Kor

eaBo

snia

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vina

_AKA

ZB

ulga

riaLe

bano

nAl

bani

aPh

ilipp

ines

Pola

ndLi

thua

nia

Rom

ania

Kaza

khst

anPo

rtuga

lS

erbi

aBo

snia

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IKy

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stan

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Arge

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itzer

land Ja

pan

New

_Zea

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arc

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th_A

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eala

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Policy and governance

Example of results triangulationIt has been identified that the legal status of organization is associated with the GDP PPP per capita factor (stratified analysis) => This association can be attributed to economic/social factors beyond the direct controlRelatively wealthy Portugal fell into a “deprived” cluster in the Policy and Governance motivated dendrogram (cluster analysis)POL3a (What is the legal status of the accreditation organization?: government agency) has been identified as highly important attribute contributing to failure (LASSO)It can be recommended to change the legal status

Challenges and extensions

Missing variablesAttributes interactionsNonlinear associations

Challenges and extensions

Missing variablesAttributes interactionsNonlinear associations

Risk factors analysisPredictive modelingRegulatory impact analysis

Questions

Date post:	19-Aug-2015
Category:	Health & Medicine
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Composite quantitative analysis of international healthcare accreditation survey data

Health & Medicine