Assessment of drinking water safety in the Netherlands using nationwide exposure and mortality data.

BACKGROUND: Although drinking water in the Netherlands is generally accepted as safe, public concern about health risks of long-term intake still exist. OBJECTIVE: The aim was to explore associations between drinking water quality for nitrate, water hardness, calcium and magnesium and causes-of-death as related to cardiovascular diseases amongst which coronary heart disease and colorectal cancer. METHODS: We used national administrative databases on cause-specific mortality, personal characteristics, residential history, social economic indicators, air quality and drinking water quality for parameters specified by the EU Drinking Water Directive. We put together a cohort of 6,998,623 persons who were at least 30 years old on January 1, 2008 and lived for at least five years on the same address. The average drinking water concentration over 2000-2010 at the production stations were used as exposure indicators. We applied age stratified Cox proportional hazards models. RESULTS: Magnesium was associated with a reduced risk for mortality due to coronary heart diseases: HR of 0.95 (95% CI: 0.90, 0.99) per 10 mg/L increase. For mortality due to cardiovascular diseases, a 100 mg/L increase in calcium was associated with a HR of 1.08 (95% CI: 1.03, 1.13) and an increase of 2.5 mmol/L of water hardness with a HR of 1.06 (95% CI: 1.01, 1.10). The results show an elevated risk for coronary heart disease mortality at calcium concentrations below 30 mg/L, but over the whole exposure range no exposure response relation was observed. For other combinations of drinking water quality parameters and cause-specific mortality studied, no statistical significant associations were identified. CONCLUSION: We identified in this explorative study a protective effect of magnesium for the risk of mortality to coronary heart disease. Also we found an increased risk of mortality due to cardiovascular disease associated with the concentration of calcium and the water hardness in drinking water.

Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression.

BACKGROUND: Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered. METHODS: We used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level. RESULTS: We estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the direct estimates at the municipal level. CONCLUSIONS: Structured additive regression is a useful tool to provide small area estimates in a unified framework. We are able to produce valid nationwide small area estimates of 26 health-related indicators at neighbourhood level in the Netherlands. The results can be used for local policy makers to make appropriate health policy decisions.

Air Pollution and Mortality in Seven Million Adults: The Dutch Environmental Longitudinal Study (DUELS).

BACKGROUND: Long-term exposure to air pollution has been associated with mortality in urban cohort studies. Few studies have investigated this association in large-scale population registries, including non-urban populations. OBJECTIVES: The aim of the study was to evaluate the associations between long-term exposure to air pollution and nonaccidental and cause-specific mortality in the Netherlands based on existing national databases. METHODS: We used existing Dutch national databases on mortality, individual characteristics, residence history, neighborhood characteristics, and national air pollution maps based on land use regression (LUR) techniques for particulates with an aerodynamic diameter ≤ 10 µm (PM10) and nitrogen dioxide (NO2). Using these databases, we established a cohort of 7.1 million individuals ≥ 30 years of age. We followed the cohort for 7 years (2004-2011). We applied Cox proportional hazard models adjusting for potential individual and area-specific confounders. RESULTS: After adjustment for individual and area-specific confounders, for each 10-µg/m3 increase, PM10 and NO2 were associated with nonaccidental mortality [hazard ratio (HR) = 1.08; 95% CI: 1.07, 1.09 and HR = 1.03; 95% CI: 1.02, 1.03, respectively], respiratory mortality (HR = 1.13; 95% CI: 1.10, 1.17 and HR = 1.02; 95% CI: 1.01, 1.03, respectively), and lung cancer mortality (HR = 1.26; 95% CI: 1.21, 1.30 and HR = 1.10 95% CI: 1.09, 1.11, respectively). Furthermore, PM10 was associated with circulatory disease mortality (HR = 1.06; 95% CI: 1.04, 1.08), but NO2 was not (HR = 1.00; 95% CI: 0.99, 1.01). PM10 associations were robust to adjustment for NO2; NO2 associations remained for nonaccidental mortality and lung cancer mortality after adjustment for PM10. CONCLUSIONS: Long-term exposure to PM10 and NO2 was associated with nonaccidental and cause-specific mortality in the Dutch population of ≥ 30 years of age.

Lung cancer risk and past exposure to emissions from a large steel plant.

We studied the spatial distribution of cancer incidence rates around a large steel plant and its association with historical exposure. The study population was close to 600,000. The incidence data was collected for 1995-2006. From historical emission data the air pollution concentrations for polycyclic aromatic hydrocarbons (PAH) and metals were modelled. Data were analyzed using Bayesian hierarchical Poisson regression models. The standardized incidence ratio (SIR) for lung cancer was up to 40% higher than average in postcodes located in two municipalities adjacent to the industrial area. Increased incidence rates could partly be explained by differences in socioeconomic status (SES). In the highest exposure category (approximately 45,000 inhabitants) a statistically significant increased relative risk (RR) of 1.21 (1.01-1.43) was found after adjustment for SES. The elevated RRs were similar for men and women. Additional analyses in a subsample of the population with personal smoking data from a recent survey suggested that the observed association between lung cancer and plant emission, after adjustment for SES, could still be caused by residual confounding. Therefore, we cannot indisputably conclude that past emissions from the steel plant have contributed to the increased risk of lung cancer.

Noise annoyance--a modifier of the association between noise level and cardiovascular health?

OBJECTIVES: The effect modifying impact of annoyance due to aircraft noise and road traffic noise on the relationships between the aircraft noise level and road traffic noise level on the prevalence of hypertension was investigated in 4861 subjects of the HYENA study (HYpertension and Exposure to Noise near Airports). METHODS: Different models were investigated either including the noise level and noise annoyance variables separately, or simultaneously, or together with an interaction term referring to the same noise source for the noise level and the noise annoyance. RESULTS: Significant effect modification was found with respect to the association between aircraft noise and hypertension. The association was stronger in more annoyed subjects. No clear interaction was found with respect to road traffic noise. The comparison of the magnitude of the main effects (per standard deviation or inter-quartile range) of noise level and noise annoyance variables revealed stronger associations with hypertension for the noise levels. CONCLUSION: There is some indication that the noise level has a stronger predictive meaning for the relationship between noise exposure and hypertension than the reported noise annoyance (main effects). The results from the Hyena study support the hypothesis that noise annoyance acts as an effect modifier of the relationship between the noise level and hypertension.

Trends in aircraft noise annoyance: the role of study and sample characteristics.

Recently, it has been suggested that the annoyance of residents at a given aircraft noise exposure level increases over the years. The objective of the present study was to verify the hypothesized trend and to identify its possible causes. To this end, the large database used to establish earlier exposure-response relationships on aircraft noise was updated with original data from several recent surveys, yielding a database with data from 34 separate airports. Multilevel grouped regression was used to determine the annoyance response per airport, after which meta-regression was used to investigate whether study characteristics could explain the heterogeneity in annoyance response between airports. A significant increase over the years was observed in annoyance at a given level of aircraft noise exposure. Furthermore, the type of annoyance scale, the type of contact, and the response percentage were found to be sources of heterogeneity. Of these, only the scale factor could statistically account for the trend, although other findings rule it out as a satisfactory explanation. No evidence was found for increased self-reported noise sensitivity. The results are of importance to the applicability of current exposure-annoyance relationships for aircraft noise and provide a basis for decisions on whether these need to be updated.

Medication use in relation to noise from aircraft and road traffic in six European countries: results of the HYENA study.

OBJECTIVES: Studies on the health effects of aircraft and road traffic noise exposure suggest excess risks of hypertension, cardiovascular disease and the use of sedatives and hypnotics. Our aim was to assess the use of medication in relation to noise from aircraft and road traffic. METHODS: This cross-sectional study measured the use of prescribed antihypertensives, antacids, anxiolytics, hypnotics, antidepressants and antasthmatics in 4,861 persons living near seven airports in six European countries (UK, Germany, the Netherlands, Sweden, Italy, and Greece). Exposure was assessed using models with 1 dB resolution (5 dB for UK road traffic noise) and spatial resolution of 250×250 m for aircraft and 10×10 m for road traffic noise. Data were analysed using multilevel logistic regression, adjusting for potential confounders. RESULTS: We found marked differences between countries in the effect of aircraft noise on antihypertensive use; for night-time aircraft noise, a 10 dB increase in exposure was associated with ORs of 1.34 (95% CI 1.14 to 1.57) for the UK and 1.19 (1.02 to 1.38) for the Netherlands but no significant associations were found for other countries. For day-time aircraft noise, excess risks were found for the UK (OR 1.35; CI: 1.13 to 1.60) but a risk deficit for Italy (OR 0.82; CI: 0.71 to 0.96). There was an excess risk of taking anxiolytic medication in relation to aircraft noise (OR 1.28; CI: 1.04 to 1.57 for daytime and OR 1.27; CI: 1.01 to 1.59 for night-time) which held across countries. We also found an association between exposure to 24hr road traffic noise and the use of antacids by men (OR 1.39; CI 1.11 to 1.74). CONCLUSION: Our results suggest an effect of aircraft noise on the use of antihypertensive medication, but this effect did not hold for all countries. Results were more consistent across countries for the increased use of anxiolytics in relation to aircraft noise.

Annoyance due to aircraft noise has increased over the years--results of the HYENA study.

In the HYENA study (HYpertension and Exposure to Noise near Airports) noise annoyances due to aircraft and road traffic noise were assessed in subjects that lived in the vicinity of 6 major European airports using the 11-point ICBEN scale (International Commission on Biological Effects of Noise). A distinction was made between the annoyance during the day and during the night. L(den) and L(night) were considered as indicators of noise exposure. Pooled data analyses showed clear exposure-response relationships between the noise level and the noise annoyance for both exposures. The exposure-response curves for road noise were congruent with the EU standard curves used for predicting the number of highly noise annoyed subjects in European communities. Annoyance ratings due to aircraft noise, however, were higher than predicted by the EU standard curves. The data supports other findings suggesting that the people's attitude towards aircraft noise has changed over the years, and that the EU standard curve for aircraft noise should be modified.

Saliva cortisol and exposure to aircraft noise in six European countries.

BACKGROUND: Several studies show an association between exposure to aircraft or road traffic noise and cardiovascular effects, which may be mediated by a noise-induced release of stress hormones. OBJECTIVE: Our objective was to assess saliva cortisol concentration in relation to exposure to aircraft noise. METHOD: A multicenter cross-sectional study, HYENA (Hypertension and Exposure to Noise near Airports), comprising 4,861 persons was carried out in six European countries. In a subgroup of 439 study participants, selected to enhance the contrast in exposure to aircraft noise, saliva cortisol was assessed three times (morning, lunch, and evening) during 1 day. RESULTS: We observed an elevation of 6.07 nmol/L [95% confidence interval (CI), 2.32-9.81 nmol/L] in morning saliva cortisol level in women exposed to aircraft noise at an average 24-hr sound level (L(Aeq,24h)) > 60 dB, compared with women exposed to L(Aeq,24h) < or = 50 dB, corresponding to an increase of 34%. Employment status appeared to modify the response. We found no association between noise exposure and saliva cortisol levels in men. CONCLUSIONS: Our results suggest that exposure to aircraft noise increases morning saliva cortisol levels in women, which could be of relevance for noise-related cardiovascular effects.

Hypertension and exposure to noise near airports: the HYENA study.

BACKGROUND: An increasing number of people are exposed to aircraft and road traffic noise. Hypertension is an important risk factor for cardiovascular disease, and even a small contribution in risk from environmental factors may have a major impact on public health. OBJECTIVES: The HYENA (Hypertension and Exposure to Noise near Airports) study aimed to assess the relations between noise from aircraft or road traffic near airports and the risk of hypertension. METHODS: We measured blood pressure and collected data on health, socioeconomic, and lifestyle factors, including diet and physical activity, via questionnaire at home visits for 4,861 persons 45-70 years of age, who had lived at least 5 years near any of six major European airports. We assessed noise exposure using detailed models with a resolution of 1 dB (5 dB for United Kingdom road traffic noise), and a spatial resolution of 250 x 250 m for aircraft and 10 x 10 m for road traffic noise. RESULTS: We found significant exposure-response relationships between night-time aircraft as well as average daily road traffic noise exposure and risk of hypertension after adjustment for major confounders. For night-time aircraft noise, a 10-dB increase in exposure was associated with an odds ratio (OR) of 1.14 [95% confidence interval (CI), 1.01-1.29]. The exposure-response relationships were similar for road traffic noise and stronger for men with an OR of 1.54 (95% CI, 0.99-2.40) in the highest exposure category (> 65 dB; p(trend) = 0.008). CONCLUSIONS: Our results indicate excess risks of hypertension related to long-term noise exposure, primarily for night-time aircraft noise and daily average road traffic noise.

Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter.

As a part of the EXPOLIS study, a stochastic exposure-modeling framework was developed. The framework is useful to compare exposure distributions of different (sub-) populations or different scenarios, and to gain insight into population exposure distributions and exposure determinants. It was implemented in an MS-Excel workbook using @Risk add-on software. Basic concept of the framework is that time-weighted average exposure is a sum of partial exposures in the visited microenvironments. Partial exposure is determined by the concentration and the time spent in the microenvironment. In the absence of data, indoor concentrations are derived as a function of ambient concentrations, effective penetration rates and contribution of indoor sources. Framework input parameters are described by probability distributions. A lognormal distribution is assumed for the microenvironment concentrations and for the contribution of indoor sources, and a beta distribution for the time spent in a microenvironment and for the penetration factor. Mean and standard deviation values parameterize the distributions. In this paper, Latin Hypercube sampling is used for the input distributions. The outcome of the framework is an estimate of the population exposure distribution for the selected air pollutant. The framework is best suited for averaging times from 24 h upwards. Sensitivity analyses can be performed to determine the most influential factors of exposure. The application of the framework is illustrated in two examples. The EXPOLIS PM(2.5) example uses microenvironment measurement and time-activity data from the EXPOLIS study to model PM(2.5) population exposure distributions in four European cities. The results are compared to the observed personal exposure distributions from the same study. The Dutch PM(10) example uses input data from several (Dutch) databases and from literature, and shows a more complex application of the framework for comparison of scenarios and subpopulations.