Small area health analyses:
pharmacy data and exposure to transport noise
Oscar Breugelmans, Jan van de Kassteele, Danny Houthuijs, Carla van Wiechen, Marten
Marra
Centre for Environmental Health Research
Health Impact Assessment Schiphol airport (HIAS)
• Overall aim: to assess changes in environmental quality and
environmentally related health effects in relation to air traffic
• HIAS started as early as 1993
• Monitoring programme 2002-2008
- Develop a health monitoring system related to the expansion of
Schiphol airport (early 2003)
• Tools:
- Survey questionnaires in 1996, 2002, 2005
- Panel study 2002-2005
- Registry of complaints and complainants
- Small area health statistics with routinely collected data
(follow up spatial pattern of disease in relation to environmental
changes)
Study goals:
• Investigate the role of transport related noise exposure on
the geographical variation and time trends in medication use
- Focus on sleeping pills/sedatives
- Noise exposure from aircraft, road and rail
• Get experience with the use of bayesian disease mapping
models
- Based on routinely collected data
- Data analysis
Small Area Health Statistics: available data
• Health data aggregated at postal code level (< 6,000
inhabitants)
- Dispensation of medication from public pharmacies 2000 -2005
- Hospital Admissions
• Environmental exposure data
- Air traffic noise (annual average exposure: Lden/Lnight)
- Road and railway noise (annual average exposure: Lden/Lnight)
Study area
•
580 postal codes
•
Population: 3,3 million
•
Inhabitants per postal code:
5,900 inhabitants
[range 10 - 21,000]
NLR modelleergebied
55 bij 71 kilometer
Pharmacy Data
Dutch registry of medication use:
Foundation for Pharmaceutical Statistics
• data from public pharmacies: dispensation of medication
• > 90% of all public pharmacies in the Netherlands
In study area:
321 pharmacies were approached to sign consent form
Positive response: 291 (91%)
Pharmacy database 1
Per patient per year; 6-year period (2000-2005):
• 3 groups of medication:
1. medication for high blood pressure
2. sedatives / sleeping pills
3. medication for respiratory diseases
• prevalence and incidence
• gender and year of birth
• 4-digit postal code home address (privacy reasons)
Gaps in data:
• only part of all pharmacies
• dispensing general practitioners not included (mainly rural area)
• data from some individual pharmacies are incomplete
• no intramural distribution of medicines
Pharmacy database 2
•
4-digit postal code database
•
total number of patients visiting pharmacy within 1 year by age, sex;
irrespective of medication use, i.e. total number of clients
→ Correction for undercoverage due to missing pharmacy data: total
number of clients instead of total population
•
Indirect standardisation of medication use per postal code and year,
based on age and sex distribution and total number of patients visiting
a pharmacy
= expected number of medication users per postal code and year
Some figures
Total population 2004
3,327,729
Total visitors of pharmacy 1,855,624 (= 56%)
study area
2004
Total users
(Prevalence)
per 100
visitors
New users
(Incidence)
per 100
visitors
Medication for
high blood pressure
233,765
12.6%
55,489
3.0%
Sedatives /
sleeping pills
251,771
13.6%
115,775
6.2%
Medication for
respiratory
diseases
177,192
9.5%
82,585
4.5%
Transport noise: aircraft
2004
• Lden and Lnight
Lnight
< 25 dB(A)
• National Aerospace Laboratory
(NLR)
25 - 30
• Based on actual flight tracks
41 - 45
• Annual data
>= 49
• Average noise exposure per
postal code and year
- Aircraft noise model → 250 m2
grid
- Attach grid value to nearest
residential addresses
- Calculate average for all
addresses within postal code
30 - 35
35 - 41
45 - 49
Modelling aircraft noise
Transport noise: Road and railway traffic
2004
% woningen >50 dB(A)
• Lden and Lnight
< 40%
• Dutch EMPARA model
40 - 60%
• No annual data
80 - 90%
60 - 80%
>= 90%
- Empara input data do not change
each year
- Traffic volume fairly constant
• Average noise exposure per
postal code
- Calculation same as aircraft noise

Is the average noise
exposure a good indicator
for all residents within a
postal code

snelwegen
Transport noise exposure distributions (1)
Transport noise exposure distributions (2)
Socio-economic status
Immigrants
2004
2004
ses score
0.0 - 0.2
0.2 - 0.4
0.4 - 0.6
0.6 - 0.8
% autochtonen
< 50%
50 - 70%
70 - 80%
80 - 90%
>= 90%
0.8 - 1.0
Confounding factors
Modelling medication use in time and space (1)
Dependent variables:
• Number of people using medicine x (prevalence) per year
• Number of new users of medicine x (incidence) per year
Independent variables per postal code
• Noise exposure from aircraft, railway and road traffic
• year
• Measure of socio economic status
• Percentage of non-western immigrants
Modelling medication use in time and space (2)
2 alternatives:
1. Exchangeable model: Assuming no spatial dependence
2. Conditional AutoRegressive (CAR) model: modelling
spatial dependence
• Bayesian hierarchical spatial model
• Implemented in software R and WinBUGS
• Binomial event
- because sleep medication use is a non-rare event
- Usual poisson approximation does not apply
Currently running the models.
No definite results yet
Strengths
• Extent of the data: powerful
• Medication early in chain from exposure to disease
• Once the data is collected: easily accessible
- follow up coming years (… 2011)
- include other groups of medication:
e.g. cardiovascular drugs, antacids
Weaknesses
• Important confounders of health not taken into account
(smoking, BMI)
• Ecological correlations
• Patient population instead of total population
(undercoverage)
• Does the average noise exposure reflect the exposure
distribution within the postal code ?
• Not so ‘routine’ to gather and manage routinely collected
data
• Analysis models are complex; model assumptions should be
checked for each analysis (by qualified statistician)