Centre for Environmental Health Research Small area health analyses: pharmacy data and exposure to transport noise Oscar Breugelmans, Jan van de Kassteele, Danny Houthuijs, Carla van Wiechen, Marten Marra

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 Develop a health monitoring system related to the expansion of Schiphol airport (early 2003) Tools: -Survey questionnaires in 1996, 2002, Panel study 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 Hospital Admissions Environmental exposure data -Air traffic noise (annual average exposure: L den /L night ) -Road and railway noise (annual average exposure: L den /L night )

Study area 580 postal codes Population: 3,3 million Inhabitants per postal code: 5,900 inhabitants [range ,000]

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 ( ): 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 study area 2004 Total users (Prevalence) per 100 visitors New users (Incidence) per 100 visitors Medication for high blood pressure 233, %55,4893.0% Sedatives / sleeping pills 251, %115,7756.2% Medication for respiratory diseases 177,1929.5%82,5854.5% Total population 20043,327,729 Total visitors of pharmacy1,855,624 (= 56%)

Transport noise: aircraft L den and L night National Aerospace Laboratory (NLR) Based on actual flight tracks Annual data Average noise exposure per postal code and year -Aircraft noise model → 250 m 2 grid -Attach grid value to nearest residential addresses -Calculate average for all addresses within postal code

Modelling aircraft noise

Transport noise: Road and railway traffic L den and L night Dutch EMPARA model No annual data -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  

Transport noise exposure distributions (1)

Transport noise exposure distributions (2)

Confounding factors Socio-economic status Immigrants

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)