Access to emergency hospitals

A GEOSTAT 1B case study

EFGS Conference 2013

24th October

Sofia, Bulgaria

Aim and relevance

• Aim:– To demonstrate the advantages of grid statistics– To analyse the population’s geographical access to emergency

hospitals divided by age-groups and sex

• Relevance:– A potential complement to The European Core Health Indicators

(ECHI) indicator 80: Equity of access to health care services– May result in improved understanding of ECHI survey results– Can improve existing surveys

Emergency Medical Services• Emergency Medical Services (EMS) - include Emergency Hospitals (EH) and pre-hospital care (on-site care and transport) - vary in between countries

• EH have an important role in the pre-hospital care as a center for ambulance vehicles, staff and

communication

Figure 1. An example of an EMS framework

Partners’ understanding of EH

Czech Republic136 hospitals with intensive care units and internal or cardiology departments. Selected out of 184 hospitals with beds for acute care (excluding narrowly specialized or detached establishments)Estonia19 hospitals providing emergency care (included in the Estonian “Hospital Master Plan” and not ambulance stations) Finland56 hospitals’ and health centres’ emergency rooms with 24/7 service (excluding “mobile emergency rooms” in the northern part of Finland)Norway44 hospitals with emergency rooms (excluding pre-hospital services as ambulance services, emergency medical communication centres and emergency clinics “legevakt”)Bulgaria28 Centers for Emergency Health Care and regional branches in the smaller towns (in most cases the municipality centers) including medical teams with equipped vehicles

Methodology

Data used:•Emergency hospitals•1km x 1km grids and municipalities including population data divided by age groups and sex•Road network including information about speed limits

Process:

1. Establish a map with service areas based on 30-minute travelling distances from or to emergency hospitals

2. Intersect the service area with 1km x 1km grids and municipalities including population data

3. Sum up the population for the individual age groups by sex and for the total population

3. Compare results on grids with results on municipality units

Advantages of using grids

Results: Bulgaria

86

88

90

92

94

96

98

100

centroid ofmunicipality

total area ofmunicipality

centroid of grid total area of grid grid - average - twomatch options

M_00_14

M_15_64

M_65_

F_00_14

F_15_64

F_65_

Results: Czech Republic

90,0

91,0

92,0

93,0

94,0

95,0

96,0

97,0

98,0

99,0

100,0

centroid of municipality total area of municipality centroid of grid centroid of building total area of grid

MEN_TOTAL

FEMALE_TOTAL

POPUL_TOTAL

Results: Estonia

Valga Hospital

Pärnu HospitalPõl va Hospital

Narva Hospital

Jõg eva Hospital

Rakvere Hospital

Hiiu CountyHosp ital

Vil jandi Hospital

Järvam aa Hospital

Lääne County Hospital

Kuressaare Hospital

South-Estoni an Ho spi tal

Ida-V iru Central Hospt ial

Rapla County Hospital

East -Tal linn Cent ral Hospti al

Tartu University Hospital

West-Talli nn C entral Hospital

North Estonia M edical Cent re

Number of populationin 1000 x 1000 m grids Hospital

Border of local municipality unit

Hospital’s 30-minute service area

Areas beyond the hospital’s 30-minute service area

0

1 – 9

10 – 49

50 – 499

500 – 4999

5000 – 17999

%

Centroid of local municipality unit is within the service area

Grid centroid is within the service area

Service area intersects grid

Centroid of local municipality unit is within the service area(the centroid is weighted by number of population in buildings)

Service area intersects local municipality unit

80 85 90 95 100 105 110 115

Males aged 0–14

Males aged 15–64

Males aged 65 and older

Females aged 0–14

Females aged 15–64

Females aged 65 and older

Total population

Results: Finland

0

10

20

30

40

50

60

70

80

90

100

Perc

ent

%

Intersecting address/building centroids withservice areas

0,0

Intersecting 1km*1km grid centroids withservice areas

0,0

Intersecting 1km*1km grid polygons withservice areas

87,9

Intersecting municipality centroids withservice areas

72,1

Intersecting municipality polygons withservice areas

96,1

Finland

Results: Norway

Access to emergency hospitals

About the results

• This case study proves the strengths of grid statistics

• The main differences in between the countries in this accessibility study lie in:

– Geographical coverage of emergency hospitals and is partly explained by differences in defining Emergency hospitals

– Population distribution

– Size and the physical geography (e.g. hilliness, coastline, lakes) of the countries

– Road network (incl. coverage and speed limits)

Further work

To assess the equity of geographical access to health care services (ECHI) this study needs to:

•agree on how to define Emergency hospitals based on Emergency Medical Services (EMS) in each country

•include traffic load as limiting factor for the accessibility

•include emergency transports by helicopter, plane or and boat when generating Service Areas

•add an additional service area with a lower driving time distance

•Consequences of applying different confidentiality thresholds

However, this might give a better understanding of why the ECHI interviewees reply differently based on nationality