Use

of

recommended

medications

after

myocardial

infarction

in

Austria

Guidelines

recommend

long-term

use

of

beta-blockers

-LRB-

BB

-RRB-

statins

and

angiotensin-converting-enzyme-inhibitors

or

angiotensin-receptor-blockers

-LRB-

ACEI/ARB

-RRB-

after

myocardial

infarction

-LRB-

MI

-RRB-

but

data

on

their

use

after

discharge

are

scarce

From

Austrian

sickness

funds

claims

we

identified

all

acute

MI

patients

who

were

discharged

within

days

and

who

survived

days

after

MI

in

We

ascertained

outpatient

use

of

ACEI/ARBs

BBs

statins

and

aspirin

from

all

filled

prescriptions

between

discharge

and

days

post

MI

Comorbidities

were

ascertained

from

use

of

indicator

drugs

during

the

preceding

year

Multivariate

logistic

regression

was

used

to

evaluate

the

independent

determinants

of

study

drug

use

We

evaluated

MI

patients

whose

mean

age

was

-LRB-

-RRB-

years

were

men

Within

days

after

MI

filled

prescriptions

for

ACE/ARBs

for

BBs

and

for

statin

While

received

all

these

classes

and

two

of

patients

received

only

one

or

of

these

drugs

Older

age

and

presence

of

severe

mental

illness

were

associated

with

lower

use

of

all

drug

classes

Diabetics

had

greater

ACEI/ARB

use

Fewer

BBs

were

used

in

patients

with

obstructive

lung

disease

Statin

use

was

lower

in

patients

using

treatment

for

congestive

heart

failure

-LRB-

all

P

-RRB-

We

conclude

that

recommended

medications

were

underused

in

Austrian

MI

survivors

Quality

indicators

should

be

established

and

interventions

be

implemented

to

ensure

maximum

secondary

prevention

after

MI

Introduction

Cardiovascular

disease

is

among

the

leading

causes

of

premature

death

and

morbidity

in

most

Western

societies

Fortunately

the

prognosis

of

myocardial

infarction

-LRB-

MI

-RRB-

has

steadily

improved

over

the

past

decades

case

fatality

after

MI

has

decreased

and

long-term

survival

has

increased

-LSB-

-RSB-

likely

due

to

the

introduction

and

increasing

availability

and

use

of

acute

invasive

and

non-invasive

interventions

as

well

as

the

introduction

of

powerful

medications

for

secondary

prevention

-LSB-

-RSB-

Among

the

medications

for

long-term

secondary

intervention

aspirin

beta-receptor

blockers

-LRB-

BB

-RRB-

inhibitors

-LRB-

statins

-RRB-

and

angiotensin

converting

enzyme

-LRB-

ACE

-RRB-

inhibitors

have

drawn

the

most

attention

More

recently

angiotensin

receptor

blockers

-LRB-

ARB

-RRB-

have

become

available

for

use

in

those

patients

who

develop

adverse

events

from

ACE

inhibitors

ACE

inhibitors

and

ARBs

have

been

shown

to

be

equally

efficacious

and

effective

after

MI

-LSB-

-RSB-

All

these

interventions

are

highly

efficacious

in

reducing

the

recurrence

of

MI

the

subsequent

use

of

coronary

interventions

or

the

risk

of

death

and

have

been

demonstrated

to

be

highly

attractive

economic

investments

from

a

societal

perspective

-LSB-

-RSB-

Practice

guidelines

recommend

the

use

of

these

medications

for

secondary

prevention

in

patients

with

unstable

angina

or

MI

regardless

whether

the

MI

is

associated

with

ST-elevation

-LSB-

-RSB-

Considerable

variation

has

been

observed

however

in

the

use

of

these

beneficial

interventions

-LSB-

-RSB-

which

has

prompted

the

establishment

of

quality

of

care

indicators

in

some

countries

with

the

goal

to

monitor

appropriate

use

of

these

interventions

during

hospitalization

at

discharge

and

during

outpatient

follow-up

after

such

an

event

-LSB-

-RSB-

Most

relevant

in

the

long

run

however

is

the

outpatient

use

of

these

medications

after

discharge

from

MI

Several

studies

have

indicated

that

these

drugs

are

underused

for

secondary

prevention

Most

of

these

investigations

were

conducted

in

North

America

-LSB-

-RSB-

and

in

Europe

-LSB-

-RSB-

No

such

systematic

assessment

has

been

conducted

in

the

Austrian

health

care

system

except

for

a

small

local

study

-LSB-

-RSB-

We

sought

to

fill

this

void

by

studying

proportions

of

BB

statin

and

ACE

inhibitor

or

ARB

use

after

MI

in

a

large

period

cross-section

of

Austrian

MI

patients

Methods

Data

sources

Austria

is

a

social

welfare

state

that

mandates

universal

health

care

to

its

residents

-LSB-

-RSB-

Patients

are

assigned

membership

in

one

of

several

sickness

funds

dependent

on

their

type

and

location

of

current

or

former

employment

At

the

time

covered

by

this

study

there

were

sickness

funds

in

Austria

among

those

the

provincial

sickness

funds

covering

more

than

three

quarters

of

all

residents

-LRB-

Mio

of

Mio

overall

data

from

-RRB-

Most

inpatient

and

outpatient

medical

services

are

covered

and

each

service

encounter

generates

a

claim

to

the

health

care

system

Inpatient

services

are

not

paid

directly

by

sickness

funds

but

by

a

separate

entity

which

however

is

partly

funded

by

these

Thus

sickness

funds

are

also

informed

about

each

encountered

inpatient

care

Similarly

to

most

western

health

care

systems

datasets

provided

by

hospitals

contain

diagnosis

codes

admission

and

discharge

dates

Access

to

prescription

drugs

is

equal

across

sickness

funds

and

all

medications

that

are

deemed

efficacious

by

a

national

panel

are

reimbursed

The

copayment

for

medicines

per

package

was

in

in

and

in

Packages

of

chronic

medications

usually

contain

or

pills

Sickness

fund

premiums

other

coinsurance

and

copayments

vary

slightly

and

indigent

patients

can

apply

for

waiver

of

the

usually

modest

copayment

for

prescription

drugs

Few

medications

are

subject

to

prior

authorization

by

the

sickness

funds

Prescription

claims

contain

a

unique

identifier

for

the

specific

drug

the

dose

and

the

quantity

dispensed

Study

population

For

the

purpose

of

this

study

we

used

the

complete

claims

data

of

individuals

covered

by

several

of

these

sickness

funds

the

provincial

sickness

funds

-LRB-

Gebietskrankenkasse

-RRB-

of

Vienna

Lower

Austria

Upper

Austria

Styria

Burgenland

and

Carinthia

as

well

as

the

funds

covering

all

federal

employees

-LRB-

Bundesversicherungsanstalt

BVA

-RRB-

and

farmers

-LRB-

Sozialversicherung

der

Bauern

SVB

-RRB-

respectively

Cumulatively

these

sickness

funds

cover

approximately

million

members

of

the

total

Austrian

insured

population

of

million

Each

sickness

fund

separately

identified

from

their

insurance

claims

all

patients

who

were

hospitalized

and

discharged

with

a

primary

diagnosis

of

acute

MI

in

-LRB-

International

Classification

of

Diseases

xx

-RRB-

and

provided

us

with

their

relevant

anonymized

health

care

claims

data

covering

the

period

from

January

to

June

We

only

studied

each

patient

's

first

hospitalization

for

MI

-LRB-

including

direct

transfers

from

one

hospital

to

another

-RRB-

and

required

that

the

admission

date

be

between

January

and

December

We

retained

only

those

patients

whose

hospitalization

exceeded

days

and

who

were

admitted

at

an

acute

care

hospital

Including

patients

who

were

hospitalized

days

would

open

up

the

possibility

of

wrongly

selecting

patients

into

the

study

sample

who

had

not

experienced

an

MI

-LRB-

e.g.

whose

MI

was

ruled

out

or

patients

who

were

admitted

for

a

diagnostic

or

therapeutic

intervention

and

still

coded

for

acute

MI

-RRB-

Our

approach

of

identifying

patients

with

MI

has

been

validated

in

similar

claims

data

and

found

to

be

highly

accurate

-LRB-

positive

predictive

value

-RRB-

-LSB-

-RSB-

Since

we

were

interested

in

medication

use

after

MI

we

generated

a

uniform

outcomes

ascertainment

window

and

required

all

patients

to

be

discharged

within

days

and

to

have

survived

for

days

from

their

initial

admission

date

-LRB-

index

date

-RRB-

We

also

excluded

those

patients

whose

hospitalization

occurred

at

a

non-acute

care

hospital

Outcomes

use

of

recommended

medications

after

myocardial

infarction

From

all

filled

prescriptions

we

recorded

out-of-hospital

use

of

several

medications

between

date

of

discharge

and

days

after

MI

admission

aspirin

beta-blockers

statins

and

ACE

inhibitors

or

ARBs

We

also

assessed

the

total

number

of

distinct

medications

that

patients

received

among

statin

BB

and

ACE

inhibitor/ARB

-LRB-

minimum

maximum

-RRB-

Since

aspirin

was

available

for

a

price

that

was

below

the

amount

of

the

drug

copayment

-LRB-

-RRB-

it

is

possible

that

insurance

claims

data

may

lead

to

under-ascertainment

of

aspirin

use

Thus

we

decided

to

investigate

aspirin

separately

and

only

among

patients

who

had

their

copayment

waived

based

on

income

grounds

These

indigent

patients

had

a

clear

economic

incentive

to

fill

prescriptions

for

aspirin

via

the

prescription

route

thus

generating

a

claim

to

the

sickness

fund

Covariates

We

created

variables

indicating

each

patient

's

age

in

gender

the

length

of

stay

for

their

index

admission

and

whether

a

patient

had

their

prescription

copayments

waived

during

the

study

period

-LRB-

NoCopay

vs.

Copay

-RRB-

Since

claims

from

outpatient

encounters

did

not

contain

any

diagnosis

codes

we

ascertained

medication

use

in

the

days

prior

to

the

index

date

as

proxies

for

several

comorbidities

using

the

anatomic

therapeutic

chemical

-LRB-

ATC

-RRB-

classification

system

oral

antidiabetic

drugs

insulin

antigout

medications

several

cardiovascular

drug

classes

-LRB-

alpha

blockers

BBs

calcium

channel

blockers

ACE

inhibitors

ARBs

other

antihypertensives

diuretics

nitrates

digitalis

vitamin

K-antagonist

statins

fibrates

-RRB-

medications

indicated

for

asthma

or

chronic

obstructive

pulmonary

disease

-LRB-

COPD

inhaled

corticosteroids

beta-receptor

agonists

-RRB-

drugs

reducing

gastro-esophageal

reflux

or

ulcer

disease

-LRB-

proton

pump

inhibitors

sucralfate

antacids

-RRB-

pain

medications

-LRB-

including

non-steroidal

anti

inflammatory

drugs

selective

inhibitors

opioids

and

others

-RRB-

oral

corticosteroids

and

several

psychoactive

drug

classes

-LRB-

benzodiazepines

or

anxiolytics

antidepressants

antipsychotics

-RRB-

We

also

recorded

the

number

of

hospital

days

during

the

year

prior

to

the

index

date

-LRB-

categorized

into

no

hospitalization

and

days

-RRB-

Statistical

analysis

We

plotted

the

unadjusted

proportions

of

medication

use

for

the

overall

population

as

well

as

by

copayment

status

along

with

the

corresponding

confidence

intervals

-LRB-

CIs

-RRB-

We

then

used

univariate

and

multivariate

logistic

regression

to

estimate

the

crude

and

multivariate

adjusted

odds

of

receiving

a

given

study

medication

Since

of

our

outcomes

were

rare

we

were

able

to

create

full

multivariate

models

that

included

all

variables

regardless

of

their

statistical

significance

In

large

datasets

where

outcomes

are

not

rare

full

multivariate

models

are

superior

to

parsimonious

models

because

they

provide

better

control

for

residual

confounding

compared

to

more

restricted

models

Odds

ratios

-LRB-

OR

-RRB-

were

presented

with

their

CIs

Additionally

we

showed

the

population

distribution

of

the

number

of

different

study

drug

classes

received

among

statin

BB

and

ACE

inhibitor/ARB

-LRB-

minimum

maximum

-RRB-

Multivariate

ordinal

logistic

regression

and

linear

regression

were

used

to

model

the

associations

between

covariates

and

the

number

of

drugs

received

All

analyses

were

conducted

in

the

full

final

study

population

as

well

as

after

restriction

to

new

users

of

each

study

drug

i.e.

patients

who

had

not

received

the

respective

study

drug

in

the

year

prior

to

admission

for

MI

We

used

the

SAS

for

Windows

-LRB-

release

-RRB-

software

for

all

statistical

analyses

-LRB-

The

SAS

Institute

Cary

NC

-RRB-

Results

Study

population

We

first

identified

hospitalizations

with

a

discharge

diagnosis

indicating

an

MI

After

excluding

all

repeat

MI

hospitalizations

and

those

that

occurred

before

or

after

-LRB-

N

-RRB-

we

also

excluded

those

patients

who

died

within

days

after

admission

for

MI

-LRB-

N

-RRB-

After

further

restricting

the

sample

to

those

patients

whose

index

hospitalization

was

but

days

and

excluding

admissions

at

non-acute

care

hospitals

we

obtained

a

final

study

sample

of

patients

-LRB-

of

the

original

hospitalization

sample

drawn

-RRB-

The

mean

age

was

years

-LRB-

standard

deviation

SD

-RRB-

and

-LRB-

-RRB-

patients

were

men

Age

differed

considerably

by

gender

men

-LRB-

median

years

interquartile

range

-RRB-

were

substantially

younger

than

women

-LRB-

median

years

interquartile

range

P

-RRB-

Overall

-LRB-

-RRB-

had

their

prescription

copayments

waived

on

the

grounds

of

low

income

-LRB-

Table

-RRB-

On

average

they

had

been

hospitalized

for

-LRB-

-RRB-

days

in

the

year

prior

to

their

MI

and

the

mean

length

of

stay

for

their

index

admission

was

-LRB-

-RRB-

days

The

proportions

of

selected

prescription

drugs

used

in

the

year

prior

to

admission

for

MI

are

listed

in

detail

in

Table

Table

of

study

population

-LRB-

N

-RRB-

VariableCount

-LRB-

-RRB-

or

mean

-LRB-

SD

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Male

-LRB-

-RRB-

Length

of

-LRB-

-RRB-

Days

of

hospitalization

in

prior

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Copayment

-LRB-

-RRB-

Previous

medication

use

Alpha

-LRB-

-RRB-

ACE-inhibitor

or

-LRB-

-RRB-

-LRB-

-RRB-

Calcium

channel

-LRB-

-RRB-

Other

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Acetylsalicylic

-LRB-

-RRB-

Clopidogrel

or

-LRB-

-RRB-

Vitamin

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Oral

hypoglycemic

-LRB-

-RRB-

-LRB-

-RRB-

Uric

acid

lowering

-LRB-

-RRB-

Pain

-LRB-

-RRB-

Gastroprotective

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Benzodiazepine

or

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Secondary

prevention

after

myocardial

infarction

Within

days

of

their

admission

for

MI

-LRB-

CI

-RRB-

patients

filled

a

prescription

for

a

BB

-LRB-

CI

-RRB-

for

an

ACE

inhibitor

or

an

ARB

and

-LRB-

CI

-RRB-

received

a

statin

-LRB-

Fig.

-RRB-

Indigent

patients

who

were

not

required

to

pay

a

copayment

received

more

ACE

inhibitors

or

ARBs

-LRB-

vs.

P

-RRB-

and

slightly

fewer

statins

-LRB-

vs.

P

-RRB-

while

the

proportion

of

BB

use

was

similar

to

patients

without

such

a

waiver

-LRB-

P

-RRB-

Fig.

of

medication

use

after

myocardial

infarction

The

distribution

of

number

of

distinct

drug

classes

received

in

this

population

was

as

follows

patients

-LRB-

-RRB-

received

a

BB

statin

and

an

ACE

inhibitor

or

ARB

while

-LRB-

-RRB-

received

two

of

these

drugs

Six

hundred

and

sixty

-LRB-

-RRB-

patients

received

only

one

of

the

study

drugs

whereas

-LRB-

-RRB-

patients

received

of

these

medications

for

secondary

prevention

at

all

This

distribution

did

not

differ

between

patients

with

vs.

without

copayment

requirement

-LRB-

P

-RRB-

The

number

of

drugs

received

was

slightly

higher

among

men

compared

to

women

-LRB-

vs.

P

-RRB-

and

decreased

with

higher

age

-LRB-

P

-RRB-

Among

the

patients

with

waived

copayment

-LRB-

-RRB-

filled

a

prescription

for

aspirin

within

days

of

admission

for

MI

Independent

predictors

of

study

medication

use

Age

was

an

independent

predictor

of

recommended

medication

use

after

MI

compared

to

patients

younger

than

years

those

between

and

years

had

greater

odds

of

receiving

ACE

inhibitors

or

ARBs

-LRB-

OR

-RRB-

whereas

the

oldest

patients

-LRB-

years

old

-RRB-

were

least

likely

to

receive

these

drugs

-LRB-

OR

Table

-RRB-

Similarly

these

oldest

patients

were

also

markedly

less

likely

to

receive

BBs

and

statins

-LRB-

Tables

and

-RRB-

Number

of

days

spent

in

the

hospital

in

the

year

preceding

the

MI

admission

a

marker

of

preexisting

comorbidity

was

consistently

and

inversely

associated

with

lower

use

of

all

these

recommended

medications

-LRB-

all

P

for

trend

-RRB-

Patients

who

had

their

copayments

waived

were

more

likely

to

fill

a

prescription

for

an

ACE

inhibitor

or

an

ARB

after

discharge

from

MI

than

patients

with

copayment

required

-LRB-

OR

CI

-RRB-

but

BB

or

statin

use

did

not

differ

between

these

groups

Angiotensin

converting

enzyme

inhibitor

or

ARB

use

was

further

independently

associated

with

previous

calcium

channel

blocker

use

use

of

an

oral

antidiabetic

drug

and

prior

use

of

antipsychotic

drugs

-LRB-

Table

-RRB-

Beta-receptor

blockers

were

more

likely

to

be

used

in

patients

who

had

used

other

antihypertensive

medications

prior

to

MI

-LRB-

Table

-RRB-

Lower

use

of

BBs

was

observed

in

patients

who

had

received

diuretics

vitamin

K-antagonist

insulin

antipsychotics

as

well

as

in

those

patients

who

received

inhaled

medications

for

asthma

or

COPD

-LRB-

Table

-RRB-

Medications

that

were

inversely

associated

with

statin

use

were

diuretics

digitalis

clopidogrel

vitamin

K-antagonist

and

antipsychotics

-LRB-

Table

-RRB-

Table

determinants

of

ACE-inhibitor

or

ARB

useVariableAll

patients

-LRB-

N

-RRB-

New

users

-LRB-

N

-RRB-

CIAge

Referent

Male

Length

of

Hospital

days

Referent

Copayment

or

Calcium

Other

anti-hypertensive

Vitamin

Oral

P

for

trend

Table

determinants

of

beta-blocker

useVariableAll

patients

-LRB-

N

-RRB-

New

users

-LRB-

N

-RRB-

CIAge

Referent

Male

Length

of

Hospital

days

Referent

Copayment

or

Calcium

Other

anti-hypertensive

Vitamin

Oral

P

for

trend

Table

determinants

of

statin

useVariableAll

patients

-LRB-

N

-RRB-

New

users

-LRB-

N

-RRB-

CIAge

Referent

Male

Length

of

Hospital

days

Referent

Copayment

or

Calcium

Other

anti-hypertensive

Vitamin

Oral

P

for

trend

Naturally

prior

use

of

a

medication

class

was

highly

predictive

of

use

of

that

class

after

MI

in

the

full

study

population

Results

from

the

analyses

restricted

to

new

users

of

each

study

drug

were

very

similar

to

the

full

population

analyses

albeit

with

wider

confidence

intervals

-LRB-

Tables

-RRB-

Analyses

of

independent

predictors

of

number

of

recommended

drugs

received

using

linear

or

ordinal

logistic

regression

confirmed

what

could

be

gleaned

from

the

analyses

of

each

drug

class

older

age

more

hospital

days

in

the

previous

year

indicator

drugs

for

severe

heart

failure

-LRB-

digitalis

diuretics

vitamin

K-antagonist

-RRB-

and

antipsychotic

use

were

all

independently

associated

with

fewer

recommended

medications

received

-LRB-

results

not

shown

-RRB-

Gender

however

was

not

independently

associated

with

number

of

medications

received

Discussion

In

a

large

population-based

study

of

patients

who

experienced

an

acute

MI

in

Austria

we

found

that

ambulatory

use

of

several

recommended

medications

after

discharge

was

suboptimal

Within

days

after

their

MI

only

patients

received

a

BB

a

statin

and

filled

a

prescription

for

an

ACE

inhibitor

or

ARB

Only

of

patients

received

all

three

interventions

while

of

patients

received

only

one

of

these

beneficial

drugs

or

even

at

all

Underuse

of

these

medications

is

unfortunate

from

both

a

patient

's

and

a

societal

perspective

while

optimal

secondary

prevention

including

these

drugs

prolongs

the

expected

lifespan

of

a

patient

after

MI

use

of

these

interventions

constitutes

an

attractive

allocation

of

scarce

economic

resources

Thus

considerable

room

for

improvement

is

present

in

the

care

of

patients

after

MI

in

Austria

and

interventions

ought

to

be

targeted

towards

increasing

the

prescribing

and

use

of

these

medications

While

this

is

the

first

large-scale

study

of

the

quality

of

post-MI

care

in

Austria

similar

investigations

have

been

conducted

in

other

countries

The

most

relevant

comparison

can

be

drawn

with

results

from

the

EUROASPIRE

studies

-LSB-

-RSB-

These

studies

three

waves

have

been

conducted

to

date

used

surveys

to

evaluate

coronary

care

in

-LRB-

EUROASPIRE

I

-RRB-

-LRB-

EUROASPIRE

II

-RRB-

and

-LRB-

EUROASPIRE

III

-RRB-

countries

respectively

Austria

did

not

participate

in

either

of

these

The

most

recent

data

available

are

from

EUROASPIRE

II

which

covered

the

years

-LSB-

-RSB-

Medication

use

was

assessed

at

admission

discharge

-LRB-

abstracted

from

charts

-RRB-

and

at

least

months

-LRB-

from

patient

interview

-RRB-

after

the

acute

admission

or

procedure

While

some

variation

was

observed

across

countries

data

specifically

for

after

MI

drug

use

was

only

reported

in

aggregate

across

countries

Approximately

of

MI

patients

received

a

BB

at

discharge

and

months

after

the

event

these

proportions

are

similar

to

the

observed

in

our

study

For

ACEI

the

proportions

at

discharge

and

months

after

the

event

were

and

respectively

considerably

lower

than

the

observed

in

Austria

It

is

unclear

however

whether

ARB

use

was

regarded

equivalent

to

ACEIs

and

included

in

this

quality

measure

in

the

EUROASPIRE

study

Lipid-lowering

drugs

were

prescribed

to

of

patients

at

discharge

and

used

by

at

months

after

the

event

compared

to

in

our

study

-LSB-

-RSB-

A

comparison

of

these

reports

however

needs

to

be

conducted

with

caution

Considerable

differences

exist

between

these

and

other

studies

especially

in

their

population

selection

criteria

ascertainment

of

medication

use

and

the

year

-LRB-

s

-RRB-

studied

Several

studies

specifically

from

the

United

States

and

Canada

investigated

medication

use

only

in

older

patients

-LRB-

years

-RRB-

-LSB-

-RSB-

Results

from

elderly

populations

can

not

be

compared

with

findings

from

the

general

population

since

age

is

a

strong

predictor

of

preventive

medication

use

Studies

that

measured

medication

use

using

discharge

notes

are

likely

to

overestimate

medication

use

because

patients

may

not

have

received

a

prescription

or

had

chosen

to

not

fill

it

The

increasing

use

of

the

study

medications

also

needs

to

be

taken

into

consideration

and

in

this

respect

older

reports

considerably

vary

with

recent

ones

To

this

point

Gislason

et

al.

-LSB-

-RSB-

studied

MI

patients

in

Denmark

and

found

that

the

proportion

of

patients

who

received

an

ACE

inhibitor

increased

from

in

to

in

Similarly

use

of

BB

increased

from

to

during

that

time

This

study

differs

from

ours

in

that

drug

use

was

ascertained

within

days

following

the

discharge

date

from

MI

While

BB

use

is

roughly

similar

to

our

findings

the

use

of

ACE

inhibitor

was

substantially

lower

in

that

Danish

cohort

Statin

or

aspirin

use

was

not

part

of

that

study

but

a

separate

study

using

the

same

Danish

database

focused

on

this

aspect

of

post

MI

care

In

this

report

statin

use

was

ascertained

within

months

of

discharge

and

increased

from

in

to

in

-LSB-

-RSB-

This

percentage

is

similar

to

the

statin

use

of

in

our

Austrian

sample

given

that

our

data

are

slightly

more

recent

we

did

however

use

a

shorter

ascertainment

window

Another

report

studied

the

use

of

preventive

medications

after

MI

in

The

Netherlands

-LSB-

-RSB-

The

authors

confirmed

a

trend

towards

increased

use

of

all

drug

classes

from

to

with

ACE

inhibitor

being

dispensed

to

BB

to

and

statins

to

in

the

most

recent

study

year

-LRB-

-RRB-

Use

of

these

medications

was

ascertained

within

days

after

discharge

but

patients

were

not

required

to

survive

for

a

minimum

number

of

days

after

discharge

to

be

eligible

for

study

Taking

these

study

design

differences

into

account

it

appears

that

more

patients

received

a

BB

after

discharge

in

the

Netherlands

compared

to

Austria

Interestingly

neither

the

Dutch

nor

the

Danish

study

took

ARB

use

into

consideration

as

an

established

substitute

for

ACE

inhibitors

Similarly

data

from

EUROASPIRE

I

and

II

confirms

increasing

use

these

preventive

medications

-LSB-

-RSB-

In

the

present

study

we

also

identified

several

predictors

of

use

for

the

study

medications

In

general

patients

years

of

age

were

substantially

less

likely

to

receive

these

recommended

medications

than

younger

patients

Similar

evidence

for

under-treatment

among

the

elderly

was

also

found

in

other

countries

-LSB-

-RSB-

This

behavior

may

constitute

general

treatment

bias

against

very

old

patients

or

reflect

that

these

patients

may

not

live

long

enough

to

reap

the

benefits

of

long-term

preventive

interventions

Indeed

specific

evidence

on

the

efficacy

and

cost-effectiveness

of

the

study

medications

in

very

old

patients

is

lacking

In

general

old

patients

were

excluded

from

participation

in

virtually

all

efficacy

trials

on

which

current

clinical

practice

recommendations

are

based

Only

the

PROspective

Study

of

Pravastatin

in

the

Elderly

at

Risk

-LRB-

PROSPER

-RRB-

trial

has

addressed

this

issue

and

specifically

studied

older

individuals

PROSPER

demonstrated

that

statins

are

efficacious

in

prevention

of

coronary

events

in

older

patients

but

the

participants

in

PROSPER

were

still

only

years

at

enrollment

-LSB-

-RSB-

Further

statin

therapy

has

been

shown

to

be

cost-effective

in

years

old

patients

-LSB-

-RSB-

We

found

that

those

indigent

patients

who

had

their

copayment

waived

had

at

least

the

same

level

of

medication

use

compared

to

relatively

more

affluent

patients

who

were

responsible

for

the

copayment

portion

of

their

prescription

-LRB-

-RRB-

thus

indicating

that

such

a

waiver

successfully

removed

the

economic

barriers

to

filling

these

prescriptions

Of

note

patients

whose

copayment

was

waived

were

more

likely

to

be

women

and

previous

recipients

of

antipsychotic

drugs

but

otherwise

similar

to

those

who

were

required

to

pay

a

copayment

-LRB-

detailed

results

not

shown

-RRB-

The

number

of

hospital

days

in

the

year

prior

to

MI

was

a

strong

negative

predictor

of

medication

use

for

all

classes

This

might

reflect

greater

comorbidity

or

frailty

in

these

patients

which

both

have

been

associated

with

lower

use

of

and

persistence

with

preventive

medications

Similarly

patients

receiving

antipsychotic

drugs

had

a

lower

likelihood

to

receive

the

study

medications

likely

indicating

treatment

bias

regarding

the

mentally

diseased

Beta-blocker

use

was

significantly

lower

among

patients

who

received

any

drugs

for

inhalation

that

are

indicated

in

asthma

or

COPD

a

plausible

pattern

which

may

reflect

presence

of

a

relative

contraindication

or

intolerance

by

the

patient

Indeed

among

patients

without

previous

use

of

such

asthma

or

COPD

drugs

-LRB-

N

-RRB-

BB

use

after

MI

was

-LRB-

rather

than

in

the

overall

population

-RRB-

an

only

slightly

higher

proportion

with

respect

to

the

goal

of

appropriately

treating

all

patients

free

from

contraindications

Statins

were

less

likely

to

be

used

in

patients

who

had

previously

used

diuretics

digitalis

or

coumadin

possibly

indicating

congestive

heart

failure

or

atrial

fibrillation

and

thus

worse

prognosis

In

theory

and

barring

presence

of

any

absolute

contraindications

of

patients

could

receive

each

of

these

medications

Clearly

the

actual

proportion

that

could

be

attained

in

practice

is

lower

than

that

since

some

patients

will

have

an

absolute

contraindication

for

any

given

drug

For

ACEI/ARBs

this

number

would

be

very

low

since

the

only

absolute

contraindication

-LRB-

other

than

pregnancy

-RRB-

is

history

of

angioedema

whose

incidence

is

below

among

new

ACEI

users

-LSB-

-RSB-

Beyond

this

relative

contraindications

may

exist

such

as

hyperkalemia

or

advanced

chronic

kidney

disease

but

in

most

patients

at

least

a

low-dose

trial

of

an

ACEI/ARB

should

be

attempted

with

appropriate

clinical

and

laboratory

monitoring

in

place

possibly

in

combination

with

a

loop

diuretic

Similarly

for

statins

practically

every

one

should

receive

this

treatment

and

absolute

contraindications

are

either

very

rare

-LRB-

active

liver

disease

-RRB-

or

not

to

be

expected

in

this

MI

population

-LRB-

pregnancy

lactation

-RRB-

Thus

at

least

an

attempt

at

using

statins

and

ACEI/ARB

in

post-MI

patients

can

be

expected

in

probably

of

patients

The

situation

is

slightly

different

with

BBs

since

more

contraindications

exist

whose

classification

as

absolute

vs.

relative

are

uncertain

-LSB-

-RSB-

Cardiogenic

shock

hypotension

and

certain

bradycardic

arrhythmias

untreated

with

a

pacemaker

certainly

constitute

absolute

contraindications

whereas

a

treatment

attempt

in

patients

with

stable

obstructive

lung

disease

should

be

conducted

From

our

dataset

it

is

difficult

to

ascertain

most

of

these

conditions

but

when

eliminating

all

patients

who

had

received

inhalative

corticosteroids

or

beta

mimetics

the

percentage

of

BB

users

increased

slightly

to

from

in

the

overall

population

This

report

needs

to

be

read

with

several

limitations

in

mind

Aspirin

was

available

at

a

price

below

the

copayment

for

prescription

medications

Thus

aspirin

use

may

be

underascertained

in

non-indigent

patients

We

were

however

able

to

assess

aspirin

use

in

those

patients

who

had

their

copayment

waived

and

found

that

of

patients

received

this

drug

While

we

can

not

be

certain

that

medications

received

in

the

pharmacy

were

actually

taken

by

these

patients

our

way

of

ascertaining

medication

use

is

superior

to

other

methods

Compared

to

notes

in

medical

charts

patients

may

not

fill

the

prescriptions

they

were

given

In

surveys

patients

may

report

what

they

perceive

as

being

desirable

rather

than

their

actual

behavior

Both

methods

may

lead

to

inaccurately

optimistic

estimates

of

medication

use

In

our

study

comorbidities

were

not

ascertained

from

diagnosis

codes

but

rather

from

typical

medications

that

are

given

for

several

comorbidities

It

has

been

shown

that

the

information

from

diagnosis

codes

and

from

medication

claims

provides

only

marginally

inferior

confounding

control

in

administrative

datasets

-LSB-

-RSB-

On

a

similar

note

we

were

unable

to

ascertain

reliable

information

on

contraindications

for

the

study

drugs

Since

most

of

these

contraindications

are

relative

rather

than

absolute

contraindications

-LSB-

-RSB-

this

aspect

may

be

of

minor

importance

While

not

fully

population

based

our

data

differ

from

other

studies

in

that

a

wide

range

of

hospitals

and

typical

care

settings

were

studied

rather

than

single

departments

in

predominantly

academic

medical

centers

We

consider

our

data

generalizable

to

the

population

level

since

three

quarters

of

the

Austrian

population

were

included

in

the

study

and

the

preponderance

of

patients

omitted

-LRB-

Mio.

data

from

-RRB-

was

excluded

because

they

lived

in

the

three

Western

provinces

-LRB-

Salzburg

Tyrol

Vorarlberg

-RRB-

whose

sickness

funds

did

not

participate

in

our

data

collection

effort

The

remaining

patients

that

we

were

unable

to

capture

were

members

of

very

small

corporate

sickness

funds

railroad

and

mining

workers

as

well

as

self-employed

individuals

all

those

however

received

care

at

the

same

hospitals

and

by

the

same

physicians

as

the

enrolled

patients

Finally

the

analytical

dataset

did

not

contain

linkable

pseudoanonymized

hospital

identifiers

Thus

we

were

unable

to

study

variations

in

treatment

behavior

across

the

provider

level

It

has

been

shown

that

provider

preference

can

be

a

stronger

determinant

of

treatment

received

than

actual

patient

level

indications

-LSB-

-RSB-

In

summary

we

provide

evidence

for

underuse

of

several

recommended

medications

after

MI

as

recently

as

in

the

Austrian

healthcare

system

Our

observations

are

in

line

with

findings

from

other

European

and

North

American

healthcare

systems

despite

the

differences

in

data

collection

and

time

period

studied

Educational

efforts

need

to

be

directed

at

both

physicians

and

patients

and

the

implementation

of

quality

indicators

should

be

considered

Maximizing

secondary

prevention

after

MI

is

highly

desirable

from

an

individual

patient

and

the

societal

perspective

O O O O O B I O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O

myocardial infarction
statins
angiotensin receptor blockers
secondary prevention

angiotensin converting enzyme inhibitors


Case-control study of environmental and social factors influencing cryptosporidiosis We report on the first case-control study to investigate the role of wider environmental and socioeconomic factors upon human cryptosporidiosis . Using GIS the detailed locations of 3368 laboratory-confirmed cases were compared to the locations of an equal number of controls . All cases were genotyped enabling Cryptosporidium hominis and Cryptosporidium parvum to be examined separately . When all cryptosporidiosis cases were analyzed , several location variables were strongly associated with illness : areas with many higher socioeconomic status individuals , many individuals aged less than 4 years , areas with a high estimate of Cryptosporidium applied to land from manure , and areas with poorer water treatment . For C. hominis cases , the strongly significant risk factors were areas with many higher socioeconomic status individuals , areas with many young children and urban areas . Socioeconomic status and areas with many individuals aged less then 4 years had a greater impact for infection with C. hominis than for C. parvum . Policy implications are discussed . Introduction Cryptosporidiosis is a common gastrointestinal disease , and is widespread in many developed and developing countries -LSB- 1 -RSB- . Our understandings of the causes of cryptosporidiosis are based upon a large number of outbreak studies and a smaller number of case-control studies . These have highlighted risks from drinking water from poorly treated public and private supplies , swimming in swimming pools , contact with farm animals and spread within institutions such as day care centres . Contact with young children is also known to be a risk factor -LSB- 2 -RSB- . There are 16 species of Cryptosporidium but C. hominis and C. parvum are the most important pathogens for humans -LSB- 3 -RSB- . In England and Wales there is a large environmental reservoir of C. parvum in livestock and transmission is related to direct or indirect contact -LRB- e.g. through faecally contaminated drinking water -RRB- with these animals . The only major environmental reservoir for C. hominis is humans , and so infection is acquired through direct or indirect contact with other infected or colonised humans particularly when associated with foreign travel -LSB- 2 , 4 -RSB- . For both species once a primary infection has occurred , secondary infections may result from person-to-person transmission . Epidemiological studies into cryptosporidiosis have used questionnaires to identify proximal risk factors -LRB- patient activity -RRB- for cryptosporidiosis such as drinking water consumption or farm visits -LSB- 5 -RSB- . The objective of this study was to investigate the role of wider environmental and socioeconomic factors -LRB- e.g. water supply , socioeconomic status , land use , livestock densities and healthcare accessibility -RRB- upon human cryptosporidiosis . This was achieved by linking small area data with postcoded cryptosporidiosis cases . All cases were genotyped to the species level enabling us to explore the aetiologies of the two main species C. hominis and C. parvum separately . We used a Geographical Information System -LRB- GIS -RRB- to derive detailed and aetiologically relevant indicators of environmental sources and points of contamination . We generated detailed measures of drinking water risks by tracing each individual 's mains water supply back to the water treatment works and into the source catchment . Background The incidence of cryptosporidiosis varies geographically and studies have indicated that C. hominis is more common in urban areas and C. parvum in rural areas -LSB- 6 , 7 -RSB- . However , few studies have examined the reasons behind this . The C. parvum excess in rural areas may be related to higher probabilities of animal contact -LSB- 6 -RSB- , a risk factor identified in a case-control study in England and Wales -LSB- 2 -RSB- . Higher population densities in urban areas may lead to greater person -- to-person transmission for C. hominis . Socioeconomic status varies between geographical areas and this may lead to differences in reported incidence of disease . Higher social classes are more likely to undertake foreign travel -LSB- 8 -RSB- and use swimming pools -LSB- 9 -RSB- ; known risk factors for cryptosporidiosis , especially C. hominis . Conversely , individuals of higher social class are more likely to consume fresh fruit and vegetables -LSB- 10 -RSB- , known to be negatively associated with cryptosporidiosis -LSB- 2 -RSB- . Agricultural workers are more likely to be exposed to Cryptosporidium -LSB- 11 -RSB- and their distribution varies spatially . Individuals with poor access to healthcare are less likely to seek treatment -LSB- 12 -RSB- and this may vary geographically or socially . Water supplies are a well-known risk factor for cryptosporidiosis -LSB- 13 -RSB- and the raw water quality and the efficiency and efficacy of treatment systems vary . Biofilms , organic coatings in water distribution systems , may play a role in prolonging the presence of Cryptosporidium in water distribution systems -LSB- 14 -RSB- . The probability of biofilm build-up is likely to be higher in long water distribution systems . Methods Species specific cryptosporidiosis incidence data were obtained from the UK Cryptosporidium reference unit for the 4 year period from January 2000 , collected as part of the national collection of Cryptosporidium oocysts -LSB- 15 -RSB- . These data represent just under half of all the cases reported to national surveillance in England and Wales over that period -LRB- Chalmers et al. in preparation -RRB- . From the original dataset of 8075 cases , 3343 were analysed once a number of records were removed from the analysis . All cases where the patient had reported recent foreign travel -LRB- 22 % -RRB- were removed from the sample as the infection may have been acquired overseas . These data were based upon information collected by laboratories from the faecal sample request form that was filled in by the patient or physician . It is likely , based on information from previous work , that travel information is under-reported . Further cases were removed due to gaps in the geographical coverage of the independent variables -LRB- 5 % -RRB- and the inability to identify the species using PCR-based methods -LRB- 2 % -RRB- . However , the main reason for the removal of cases were records which , had incomplete postcodes -LRB- 59 % -RRB- meaning that it was impossible to identify an accurate residential location . It is important to consider whether this omission may be correlated with any of our independent variables and hence introduce bias into the results . One source of missing postcodes is transcription omissions at individual laboratories but because these are likely to affect samples in a random manner they are unlikely to be correlated with any independent variables . The second major source is omissions by general practitioners due to them having inaccurate patient records or failing to include postcodes when submitting samples to laboratories for analysis . There is no evidence as to whether this varies systematically in a manner , which would bias the results of this research . For each case one comparator control postcode was randomly selected from within the service area of the laboratory to which the case stool sample was sent . The process was weighted to account for differences in population between postcodes . Control postcodes were selected from within the same laboratory service area as the case postcode because different laboratories have different selection criteria and screening protocols for cryptosporidiosis -LSB- 16 -RSB- and so differences in incidence between laboratory service areas may be artefactual . Service areas were generated by assuming that each sample was sent to its nearest laboratory -LRB- based on shortest road distance -RRB- . This resulted in 135 service areas across England and Wales and the method was validated successfully by comparing the postcodes of the cases , to the laboratory to which their sample was sent . This indicated that 87 % of cases were assigned to the correct laboratory . These service areas varied in size from 106 km2 to 3,770 km2 with an average size of 1,142 km2 . They were consequently large enough to contain a variety of different drinking water sources and encompass a range of agricultural landscapes and socioeconomic characteristics . Our control postcodes represent areas where no cryptosporidiosis was reported . However , due to issues of under reporting -LSB- 17 -RSB- and asymptomatic infection -LSB- 18 , 19 -RSB- some of the control postcodes may have contained affected individuals . However , the reported rate of cryptosporidiosis in England and Wales is only 9 per 100,000 per year and so even taking into account under reporting and asymptomatic infection , the probability of a control postcode containing cases remains extremely low . Furthermore , such a bias would only tend our results to the null hypothesis . For each case postcode and control postcode explanatory variables were derived using GIS -LRB- ArcGIS9 .1 -RRB- . A measure of the degree of rurality was obtained using the Rural and Urban Area Classification -LSB- 20 -RSB- . This groups census areas into one of eight categories ranging from `` urban areas '' to `` hamlets and isolated dwellings in sparse surroundings '' . A buffer zone of 2.5 km was created around the centre of each postcode and within these areas the GIS was used to extract estimates of the total amount of Cryptosporidium applied to land through animal manures . These were based upon a 1 km2 map of manure applications developed by the Agricultural Land Advisory Service -LSB- 21 -RSB- . This was created by combining information from the agricultural census with land use data , information from animal excreta and manure management surveys and estimates of oocyst concentrations in manure -LSB- 22 -RSB- . The estimates will consist of C. parvum and other species/genotypes common in animals , which are unlikely to include C. hominis . A range of socioeconomic variables were obtained by identifying the 2001 Output Area within which each postcode was located . Output areas are the smallest area for which census data are released and contain approximately 125 individuals . Within each Output Area the percentage of people in each of the eight socio-economic status bands were identified . These ranged from higher managerial and professional occupations to those in routine occupations . Additionally , the proportion of the population aged 0 -- 4 years and the percentage employed in agriculture was obtained . Finally a measure of health care accessibility was derived by calculating the travel time from each postcode to the nearest GP . Individuals receive their water from one or more water treatment works , which abstract their water from one or more sources and are subject to different forms of water treatment . Consequently , a number of volume-weighted measures were derived to describe the water supply of each case and control postcode . Information on the proportion of water supplied from different sources -LRB- surface vs. groundwater -RRB- and subject to different treatments -LRB- e.g. membrane filtration , simple disinfection -RRB- was obtained from the England and Wales Drinking Water Inspectorate . These data included any changes in treatment that occurred during the 2000 -- 2003 period . Information could only be obtained for public water supplies . For each surface or groundwater abstraction , catchments were calculated using the GIS and the density of Cryptosporidium applications to land , sewage discharges and sewage overflows in each were calculated . The probability of biofilm build-up was simulated by calculating the volume weighted , average straight line distance between each postcode and the water treatment works supplying their water . In total over 50 explanatory variables were produced . Multivariable models were then constructed using conditional logistic regression for all Cryptosporidium infections followed by separate models for C. hominis and C. parvum . A forward regression technique was performed by adding the most significant variable in turn . Collinearity was avoided by ensuring that the addition of each variable did not lead to significant changes in the coefficients or significance of any other variables in the model . Standardised forms of the independent variables were entered into the models . Results are presented in terms of Odds Ratio -LRB- OR -RRB- estimates and the 95 % Confidence Interval -LRB- CI -RRB- . The statistical analysis was undertaken using STATA/SE 8.2 . Much of our understanding of the aetiology of cryptosporidiosis is obtained from an analysis of reported outbreaks . However , these only represent about 8 % of all cases reported to national surveillance . In order to examine whether outbreak cases were significantly influencing the results presented in this paper all our models were fitted with and without outbreak cases . Cases associated with a particular outbreak are identified within the national collection of Cryptosporidium oocysts used for this study from records held by local Health Protection Teams or Units . The omission of outbreak cases had a minimal impact upon the results implying that outbreak cases are not significantly influencing the results presented . Consequently , the models presented in this paper are inclusive of all cases . Results The results for all Cryptosporidium infections are shown in Table 1 . These indicate that living in an area with higher amounts of Cryptosporidium applied to land in a 2.5 km buffer around each postcode -LRB- OR 1.084 P = 0.022 -RRB- , larger proportions of individuals in the 0 -- 4 years age group -LRB- OR 1.145 P < 0.001 -RRB- and more individuals in the highest socioeconomic status groups -LRB- OR 1.203 P < 0.001 -RRB- were all positively associated with risk . Drinking water subject to superior water treatment -LRB- OR 0.770 P < 0.001 -RRB- and groundwater sourced drinking water -LRB- OR 0.821 P = 0.001 -RRB- were negatively associated with risk . Once these two drinking water variables were included in the model the proportion of the water supply which was from non superiorly treated surface water sources was negatively associated with risk -LRB- OR 0.869 P = 0.019 -RRB- . Table 1Multivariate model for all Cryptosporidium infectionVariableOR95 % CIP valueStandardised quantity of Cryptosporidium applied to land in a 2.5 km buffer1 .0841.012 -- 1.1630.022 Standardised proportion of population in ages 0 -- 41.1451.090 -- 1.203 < 0.001 Standardised proportion of population in highest socioeconomic status groups -LRB- 1 -- 4 -RRB- a1 .2031.140 -- 1.270 < 0.001 Standardised proportion of water supply that is subject to superior treatmentb0 .7700.679 -- 0.874 < 0.001 Standardised proportion of water supply that is groundwater0 .8210.729 -- 0.9250.001 Standardised proportion of water supply from non superiorly treated surface water0 .8690.772 -- 0.9770.019 R2 = 2.23 % , estimated from 3368 case and 3368 control postcodesaWorking in managerial and professional occupations , intermediate occupations and small employers and own account workersbDefined as water treated with coagulation and flocculation , slow sand filtration , cartridge or membrane filtration The dataset was then split into C. parvum and C. hominis . The results for C. parvum are shown in Table 2 and indicate that living in an urban area was negatively associated with risk -LRB- OR 0.852 P < 0.001 -RRB- . Living in an area with higher amounts of Cryptosporidium applied to land in a 2.5 km buffer around each postcode -LRB- OR 1.167 P < 0.005 -RRB- , larger proportions of individuals in the 0 -- 4 age group -LRB- OR 1.094 P = 0.018 -RRB- and more individuals in the highest socioeconomic status groups -LRB- OR 1.109 P = 0.010 -RRB- were all positively associated with risk . Drinking water subject to superior water treatment -LRB- OR 0.738 P < 0.001 -RRB- and groundwater sourced drinking water -LRB- OR 0.679 P < 0.001 -RRB- were negatively associated with risk . Once these two drinking water variables were controlled for two further variables became significant . An interaction between the proportion of groundwater supply and the amount of Cryptosporidium applied to land in the catchment -LRB- OR 1.289 P < 0.001 -RRB- was positively associated with risk . The proportion of the water supply which was not superiorly treated , from a surface water source with a high amount of Cryptosporidium applied to land in the catchment was negatively associated with risk -LRB- OR 0.846 P = 0.009 -RRB- . Table 2Multivariate model for Cryptosporidium parvum infectionVariableOR95 % CIP valueStandardised urban area0 .8520.779 -- 0.932 < 0.001 Standardised quantity of Cryptosporidium applied to land in a 2.5 km buffer1 .1671.047 -- 1.2300.005 Standardised proportion of population in ages 0 -- 41.094 1.015 -- 1.1790.018 Standardised proportion of population in highest socioeconomic status groups -LRB- 1 -- 4 -RRB- a1 .109 1.025 -- 1.2000.010 Standardised proportion of water supply that is subject to superior treatmentb0 .738 0.646 -- 0.842 < 0.001 Standardised proportion of water supply that is groundwater0 .679 0.554 -- 0.833 < 0.001 Standardised interaction between the groundwater proportion and the C. parvum density in catchment1 .289 1.088 -- 1.527 < 0.001 Standardised proportion of water supply from non superiorly treated surface water with high C. parvum density in catchment0 .846 0.746 -- 0.9590.009 R2 = 4.24 % , estimated from 1,623 case and 1,623 control postcodesaWorking in managerial and professional occupations , intermediate occupations and small employers and own account workersbDefined as water treated with coagulation and flocculation , slow sand filtration , cartridge or membrane filtration The results for C. hominis are presented in Table 3 and indicate positive associations with living in an urban area -LRB- OR 1.261 P < 0.001 -RRB- , an area with a high proportion of individuals from social classes 1 -- 4 -LRB- OR 1.297 P < 0.001 -RRB- and many individuals in the 0 -- 4 age group -LRB- OR 1.189 P < 0.001 -RRB- . Table 3Multivariate model for Cryptosporidiumhominis infectionVariablesOR95 % CIP valueStandardised urban area1 .2611.154 -- 1.378 < 0.001 Standardised proportion of population in highest socioeconomic status groups -LRB- 1 -- 4 -RRB- a1 .2971.201 -- 1.401 < 0.001 Standardised proportion of population in ages 0 -- 41.1901.112 -- 1.274 < 0.001 R2 = 3.57 , estimated from 1,720 case and 1,720 control postcodesaWorking in managerial and professional occupations , intermediate occupations and small employers and own account workers Discussion In the full model all Cryptosporidium illness was positively associated with the quantity of Cryptosporidium applied to land in a 2.5 km buffer around each postcode . This may be due to the increased probability of direct contact with the environmental reservoir of Cryptosporidium in animal manures . When the analysis was split into C. hominis and C. parvum , surrounding Cryptosporidium applications to land was only a significant risk factor for C. parvum . This is unsurprising as animal manures are unlikely to include C. hominis . There were positive associations between cryptosporidiosis and higher social classes for all Cryptosporidium illness but when the individual species were examined the social class gradient was significantly stronger for C. hominis in comparison to C. parvum -LRB- OR 1.297 vs. 1.109 -RRB- . There are a number of explanations for this . If we consider C. parvum , then there is a large environmental reservoir and individuals of higher social classes are more likely to undertake recreational activities such as walking in the countryside -LSB- 9 -RSB- which may increase the chance of animal-to-human transmission . Additionally individuals of higher social classes are more likely to undertake foreign travel -LSB- 8 -RSB- a known risk factor for cryptosporidiosis but especially for C. hominis -LSB- 4 -RSB- . Foreign travel cases were removed from our analysis , though it is likely that some travel related cases remained within the dataset . Furthermore , the association with social class may indicate community transmission from travel cases to other individuals in the same social group with whom they are most likely to interact . This may be direct person-to-person transmission -LSB- 2 -RSB- or indirect transmission such as through swimming pools . It is known that recreational swimming is positively associated with higher social groups -LSB- 9 -RSB- . It is also worth highlighting that these positive associations with higher social classes are overriding other factors . Individuals of a higher social class are more likely to consume fresh fruit and vegetables -LSB- 10 -RSB- , known to be negatively associated with cryptosporidiosis -LSB- 2 -RSB- . Living in an area with a higher proportion of individuals aged 0 -- 4 years is a risk factor for all illness with Cryptosporidium and for C. parvum and C. hominis individually . It is known that cryptosporidiosis incidence is highest in this age group . However , the observation that the magnitude of effect is greater for C. hominis than for C. parvum -LRB- OR 1.190 vs. 1.094 -RRB- is surprising as previous studies have shown a greater proportion of C. parvum -LRB- 33 % -RRB- occur in children in the 0 -- 4 age group compared to C. hominis -LRB- 20 % -RRB- -LSB- 2 -RSB- . Toileting contact with a child under 5 years , even in the absence of symptoms , has been shown to be a risk factor for C. hominis but not C. parvum -LRB- 2 -RRB- . The authors of this study suggested that asymptomatic carriage in young children may be one of the main reservoirs of C. hominis . The association with children in the 0 -- 4 year age group was examined further by subdividing the C. parvum and C. hominis models into those cases where the affected individual was 4 years or younger and those where the individual was 5 years and older . To ensure comparability , identical explanatory variables were fitted to each sub model as in the original models . The resulting models were then analysed for major shifts in the significance of the explanatory variables . The only major difference for C. parvum was that the percentage of individuals aged 0 -- 4 in the area was not significant -LRB- P = 0.9 -RRB- in the model for those 5 years and older . This suggests that young children are not a risk factor for C. parvum illness in older individuals . In contrast the percentage aged 0 -- 4 was a significant risk factor in both young and old for C. hominis , highlighting the importance of person-to-person transmission between young and older individuals . In the 4 years and younger model , living in an urban area was an insignificant risk factor -LRB- P = 0.7 -RRB- for C. hominis . All illness with Cryptosporidium showed negative associations with areas having superior water treatment , and negative associations with areas supplied by large amounts of groundwater . When the models were split by species , this effect was observed for only C. parvum . Although there have been outbreaks of C. hominis associated with sewage contamination of drinking water , C. parvum dominates in the environment and so the lack of association between C. hominis and drinking water is unsurprising . Once water source and treatment were included in the model , further variables became significant in the overall model and in the C. parvum model . These need to be interpreted with caution as they will be greatly impacted by any measurement error in our source and treatment drinking water variables . In the overall model the proportion of poorly treated water supplied from surface water sources was negatively associated with risk . In the C. parvum model a similar variable became significant namely the proportion of poorly treated water supplied from surface water sources from catchments with high amounts of Cryptosporidium applications to land . Both these associations indicate reductions in risk from the drinking water supplies most at risk of Cryptosporidium contamination . This may highlight immunity in a population periodically exposed to Cryptosporidium -LSB- 19 -RSB- . Alternatively new Cryptosporidium regulations to improve water supplies have led to improved water treatment at sites most at risk of Cryptosporidium contamination -LSB- 23 -RSB- and these improvements have been incorporated into our water supply database . Consequently the negative association with the most at risk sites may be evidence of the new regulations leading to the improved monitoring and attention being paid to these most at risk plants -LSB- 23 -RSB- . In the C. parvum model a positive association was observed in the interaction variable between the proportion of groundwater sourced drinking water and the quantity of Cryptosporidium applied to land in the groundwater catchment . This highlights that , although generally of lower risk , if there is a high density of Cryptosporidium in the groundwater catchment then these can be a risk factor for illness with C. parvum . The links between cryptosporidiosis and drinking water are noteworthy as this period covers the new drinking water regulations to control Cryptosporidium implemented in 2000 -LSB- 23 -RSB- . Since this time a reduction in cryptosporidiosis has been reported , especially the reduction in the size of the spring peak nationally -LSB- 24 -RSB- . Consequently , although the regulations appear to have had success in reducing illness , drinking water remains a significant risk factor for cryptosporidiosis in England and Wales . The urban -- rural gradient was not a significant variable in the full model . However , in the C. parvum model negative associations were found with urban areas . It is important to note that these associations were independent of any impacts relating to water supply and Cryptosporidium applications to land , which we would expect to be correlated with urban areas . It may be that other factors associated with living in urban areas are important . It may indicate that these individuals are less likely to interact with their surroundings reducing the probability of C. parvum infection . An alternative explanation is that our water treatment variables reflect the supply pattern in 2003 . In earlier years it may be that the association with land use is accounting for the greater uncertainty in the water treatment variables . This is plausible as rural water supplies are likely to be at greater risk of C. parvum contamination from agricultural and possibly wildlife sources . This argument is strengthened by the observation that when the data were stratified into 2000 -- 2001 and 2002 -- 2003 the land use variable was only significant in the earlier period . For C. hominis a positive association with urban areas was observed . This may be due to increased opportunities for person-to-person transmission in these areas due to factors such as more nursing homes , day care centres and enhanced availability of swimming pools . The association between increased risk of a person-to person-transmitted disease and increased population density is in line with that predicted by epidemiological theory -LSB- 25 -RSB- . In the model of all illness with Cryptosporidium the urban -- rural density is not a significant variable implying that the different relationships observed for C. parvum and C. hominis are cancelling each other out in the full model . The observation of different associations with urban areas for C. parvum and C. hominis is also noteworthy because of our earlier concern about omissions of postcodes by general practitioner and whether this could bias the study results . It could be suggested that postcode completeness varies between general practitioners in urban and non-urban areas . However , the models for C. parvum and C. hominis show different signs for the urban area variable . This implies that any bias due to differential general practitioner completeness of postcode information between urban and non-urban areas is likely to be minimal . In this study no associations were found between cryptosporidiosis and the proxy variable for biofilm build-up , accessibility to healthcare or the proportion of the population employed in agriculture . This suggests that these variables are not of major importance to cryptosporidiosis aetiology or that our methodology was unable to create effective measures of them . In conclusion , this is the first study to examine the influence of the wider environment upon illness with Cryptosporidium and of the two species C. hominis and C. parvum separately . It has shown separate aetiologies for each species highlighting the importance of separating C. hominis and C. parvum in epidemiological studies . For the first time it has also demonstrated all illness with Cryptosporidium to be higher in areas populated by higher social classes and this effect is greater for illness with C. hominis . It also supports studies indicating the importance of young children to illness with both Cryptosporidium species . This is the first study to highlight the importance of the agriculture surrounding place of residence as a risk factor for C. parvum illness . Elevated incidence of C. parvum illness was found in rural areas and elevated levels of C. hominis illness in urban areas , effects that were independent of water supply and agricultural factors . The analysis highlighted that , in spite of new regulations to reduce Cryptosporidium concentrations in drinking water , water supply remained a risk factor for illness with C. parvum . Risk was lowered in areas with superior water treatment and in areas supplied by groundwater . There was also evidence that , in certain circumstances , groundwater could be a risk factor for C. parvum illness . Finally there were indications of a lowering of risk of C. parvum illness in the most at risk water supplies , an impact that was independent of superior treatment . This may be evidence of population immunity or effective regulation governing water treatment and monitoring .

O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O B O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O B O O O O O O B O O O B O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O B O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O B O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O

agriculture
cryptosporidiosis
cryptosporidium
social class
water