A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.

The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.

Trends in Temperature-associated Mortality in São Paulo (Brazil) between 2000 and 2018: an Example of Disparities in Adaptation to Cold and Heat.

Exposure to non-optimal temperatures remains the single most deathful direct climate change impact to health. The risk varies based on the adaptation capacity of the exposed population which can be driven by climatic and/or non-climatic factors subject to fluctuations over time. We investigated temporal changes in the exposure-response relationship between daily mean temperature and mortality by cause of death, sex, age, and ethnicity in the megacity of São Paulo, Brazil (2000-2018). We fitted a quasi-Poisson regression model with time-varying distributed-lag non-linear model (tv-DLNM) to obtain annual estimates. We used two indicators of adaptation: trends in the annual minimum mortality temperature (MMT), i.e., temperature at which the mortality rate is the lowest, and in the cumulative relative risk (cRR) associated with extreme cold and heat. Finally, we evaluated their association with annual mean temperature and annual extreme cold and heat, respectively to assess the role of climatic and non-climatic drivers. In total, we investigated 4,471,000 deaths from non-external causes. We found significant temporal trends for both the MMT and cRR indicators. The former was decoupled from changes in AMT, whereas the latter showed some degree of alignment with extreme heat and cold, suggesting the role of both climatic and non-climatic adaptation drivers. Finally, changes in MMT and cRR varied substantially by sex, age, and ethnicity, exposing disparities in the adaptation capacity of these population groups. Our findings support the need for group-specific interventions and regular monitoring of the health risk to non-optimal temperatures to inform urban public health policies.

Mitigating the impact of air pollution on dementia and brain health: Setting the policy agenda.

BACKGROUND: Emerging research suggests exposure to high levels of air pollution at critical points in the life-course is detrimental to brain health, including cognitive decline and dementia. Social determinants play a significant role, including socio-economic deprivation, environmental factors and heightened health and social inequalities. Policies have been proposed more generally, but their benefits for brain health have yet to be fully explored. OBJECTIVE AND METHODS: Over the course of two years, we worked as a consortium of 20+ academics in a participatory and consensus method to develop the first policy agenda for mitigating air pollution's impact on brain health and dementia, including an umbrella review and engaging 11 stakeholder organisations. RESULTS: We identified three policy domains and 14 priority areas. Research and Funding included: (1) embracing a complexities of place approach that (2) highlights vulnerable populations; (3) details the impact of ambient PM2.5 on brain health, including current and historical high-resolution exposure models; (4) emphasises the importance of indoor air pollution; (5) catalogues the multiple pathways to disease for brain health and dementia, including those most at risk; (6) embraces a life course perspective; and (7) radically rethinks funding. Education and Awareness included: (8) making this unrecognised public health issue known; (9) developing educational products; (10) attaching air pollution and brain health to existing strategies and campaigns; and (11) providing publicly available monitoring, assessment and screening tools. Policy Evaluation included: (12) conducting complex systems evaluation; (13) engaging in co-production; and (14) evaluating air quality policies for their brain health benefits. CONCLUSION: Given the pressing issues of brain health, dementia and air pollution, setting a policy agenda is crucial. Policy needs to be matched by scientific evidence and appropriate guidelines, including bespoke strategies to optimise impact and mitigate unintended consequences. The agenda provided here is the first step toward such a plan.

Using ecological propensity score to adjust for missing confounders in small area studies.

Small area ecological studies are commonly used in epidemiology to assess the impact of area level risk factors on health outcomes when data are only available in an aggregated form. However, the resulting estimates are often biased due to unmeasured confounders, which typically are not available from the standard administrative registries used for these studies. Extra information on confounders can be provided through external data sets such as surveys or cohorts, where the data are available at the individual level rather than at the area level; however, such data typically lack the geographical coverage of administrative registries. We develop a framework of analysis which combines ecological and individual level data from different sources to provide an adjusted estimate of area level risk factors which is less biased. Our method (i) summarizes all available individual level confounders into an area level scalar variable, which we call ecological propensity score (EPS), (ii) implements a hierarchical structured approach to impute the values of EPS whenever they are missing, and (iii) includes the estimated and imputed EPS into the ecological regression linking the risk factors to the health outcome. Through a simulation study, we show that integrating individual level data into small area analyses via EPS is a promising method to reduce the bias intrinsic in ecological studies due to unmeasured confounders; we also apply the method to a real case study to evaluate the effect of air pollution on coronary heart disease hospital admissions in Greater London.

A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).

Mortality Risk in Alcoholic Patients in Northern Italy: Comorbidity and Treatment Retention Effects in a 30-Year Follow-Up Study.

AIMS: To analyse the general and cause-specific mortality over the course of 30 years among subjects treated for alcohol use disorders (AUD) in Northern Italy. METHODS: Cohort of 2499 subjects followed-up for mortality until 31 December 2012. Standardized mortality ratios (SMRs) and 95% confidence intervals (CI) were computed to compare the mortality in the cohort with the general population. Cox regression was used to study the effect of psychiatric disorders, burden of physical comorbidity and retention in treatment on mortality, controlling for socio-demographic factors. RESULTS: During the follow-up, 435 deaths occurred. Compared with the general population, alcoholics experienced a 5-fold increased mortality (SMR: 5.53; 95% CI: 5.03, 6.07). Significant excess mortality was observed for a range of specific causes: infections, cancers, cardiovascular, respiratory and digestive system diseases as well as violent causes. In multivariate analysis, the hazard of dying was lower for female gender (hazard ratio [HR]: 0.62; 95% CI: 0.46, 0.84) and for increasing length of retention in treatment (HR for third tertile vs first tertile: 0.43; 95% CI: 0.32, 0.57). Burden of physical comorbidity was associated with increased hazard of dying (HR for 3+ comorbidities vs no comorbidities: 4.40; 95% CI: 2.91, 6.66). Psychiatric comorbidity was not associated with mortality. CONCLUSIONS: Despite the harmful effect of AUD, retention in treatment represented a protective factor against death, suggesting that strategies supporting primary medical- and social-care may effectively reduce premature mortality.

[Low exposure to lead and reproductive health: a cohort study of female workers in the ceramic industry of Emilia-Romagna (Northern Italy)]. / Bassa esposizione a piombo ed esiti della gravidanza: uno studio di coorte delle lavoratrici della ceramica in Emilia-Romagna.

AIMS: to assess the effects of low levels of lead exposure on reproductive health (miscarriage, fertility, multiple births, sex ratio at birth, incidence of some diseases during pregnancy), following a cohort of female workers exposed to lead in the ceramic tile industry in the Municipalities of Scandiano (RE) and Sassuolo (MO), Northern Italy. DESIGN: a cohort of 2,067 female workers was considered. These workers repeatedly underwent blood lead levels testing at the Toxicology Laboratory of Scandiano (RE) in the period 1998-2004. Follow-up was performed for each subject for the 12 months following any blood lead testing. Data on miscarriages and live births were obtained through a linkage with hospital discharge records. Results were compared with the frequency of events in the general female population in the Emilia-Romagna Region (Northern Italy). The frequency of multiple births was also examined, as well as the ratio of male-to-female infants and maternal diseases during pregnancy. An internal analysis within the cohort was conducted to evaluate the associations with increasing lead levels. RESULTS: the women under study accumulated 5,722 person-years of observation. The age distribution of study subjects was not different from the one observed in the Region. Thirty-one miscarriages and 212 live births were recorded. The miscarriage rate (5.42‰) among the study subjects was not different from the regional reference, while the fertility rate (37.05‰) was lower (RR: 0.72; 95%CI 0.63-0.83). The frequency of multiple births (1.9%) was similar to the regional rate (1.2%). Eighty-six females (40.57%) and 126 males (59.43%) were born, compared to regional percentages of 49% females and 51% males. Of all the indicators examined, only miscarriage showed a positive trend among women exposed to lead. In addition, women exposed to lead had a higher frequency of hypertension during pregnancy (RR: 1.34; 95%CI 1.07-1.68), problems with the amniotic cavity (RR: 1.16; 95%CI 1.02-1.33), and prolonged pregnancy (RR: 1.37; 95%CI 1.09-1.73). CONCLUSIONS: the cohort of female subjects under study showed rate of miscarriage similar to the general population and a lower fertility rate. There were a higher percentage of male births and an increase of some conditions during pregnancy possibly related to lead exposure.

A framework for estimating and visualising excess mortality during the COVID-19 pandemic.

COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.

Spatiotemporal bayesian modelling of scorpionism and its risk factors in the state of São Paulo, Brazil.

BACKGROUND: Scorpion stings in Brazil represent a major public health problem due to their incidence and their potential ability to lead to severe and often fatal clinical outcomes. A better understanding of scorpionism determinants is essential for a precise comprehension of accident dynamics and to guide public policy. Our study is the first to model the spatio-temporal variability of scorpionism across municipalities in São Paulo (SP) and to investigate its relationship with demographic, socioeconomic, environmental, and climatic variables. METHODOLOGY: This ecological study analyzed secondary data on scorpion envenomation in SP from 2008 to 2021, using the Integrated Nested Laplace Approximation (INLA) to perform Bayesian inference for detection of areas and periods with the most suitable conditions for scorpionism. PRINCIPAL FINDINGS: From the spring of 2008 to 2021, the relative risk (RR) increased eight times in SP, from 0.47 (95%CI 0.43-0.51) to 3.57 (95%CI 3.36-3.78), although there has been an apparent stabilization since 2019. The western, northern, and northwestern parts of SP showed higher risks; overall, there was a 13% decrease in scorpionism during winters. Among the covariates considered, an increase of one standard deviation in the Gini index, which captures income inequality, was associated with a 11% increase in scorpion envenomation. Maximum temperatures were also associated with scorpionism, with risks doubling for temperatures above 36°C. Relative humidity displayed a nonlinear association, with a 50% increase in risk for 30-32% humidity and reached a minimum of 0.63 RR for 75-76% humidity. CONCLUSIONS: Higher temperatures, lower humidity, and social inequalities were associated with a higher risk of scorpionism in SP municipalities. By capturing local and temporal relationships across space and time, authorities can design more effective strategies that adhere to local and temporal considerations.

Regional excess mortality during the 2020 COVID-19 pandemic in five European countries.

The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in the first stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.

Long-term exposure to air pollution and COVID-19 incidence: A multi-country study.

The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.

Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.

In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016-2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.

Income level and chronic ambulatory care sensitive conditions in adults: a multicity population-based study in Italy.

BACKGROUND: A relationship between quality of primary health care and preventable hospitalizations has been described in the US, especially among the elderly. In Europe, there has been a recent increase in the evaluation of Ambulatory Care Sensitive Conditions (ACSC) as an indicator of health care quality, but evidence is still limited. The aim of this study was to determine whether income level is associated with higher hospitalization rates for ACSC in adults in a country with universal health care coverage. METHODS: From the hospital registries in four Italian cities (Turin, Milan, Bologna, Rome), we identified 9384 hospital admissions for six chronic conditions (diabetes, hypertension, congestive heart failure, angina pectoris, chronic obstructive pulmonary disease, and asthma) among 20-64 year-olds in 2000. Case definition was based on the ICD-9-CM coding algorithm suggested by the Agency for Health Research and Quality - Prevention Quality Indicators. An area-based (census block) income index was used for each individual. All hospitalization rates were directly standardised for gender and age using the Italian population. Poisson regression analysis was performed to assess the relationship between income level (quintiles) and hospitalization rates (RR, 95% CI) separately for the selected conditions controlling for age, gender and city of residence. RESULTS: Overall, the ACSC age-standardized rate was 26.1 per 10.000 inhabitants. All conditions showed a statistically significant socioeconomic gradient, with low income people being more likely to be hospitalized than their well off counterparts. The association was particularly strong for chronic obstructive pulmonary disease (level V low income vs. level I high income RR = 4.23 95%CI 3.37-5.31) and for congestive heart failure (RR = 3.78, 95% CI = 3.09-4.62). With the exception of asthma, males were more vulnerable to ACSC hospitalizations than females. The risks were higher among 45-64 year olds than in younger people. CONCLUSIONS: The socioeconomic gradient in ACSC hospitalization rates confirms the gap in health status between social groups in our country. Insufficient or ineffective primary care is suggested as a plausible additional factor aggravating inequality. This finding highlights the need for improving outpatient care programmes to reduce the excess of unnecessary hospitalizations among poor people.

[Cancer incidence estimation by hospital discharge flow as compared with cancer registries data]. / Stima dell'incidenza del carcinoma mammario attraverso il flusso dei ricoveri ospedalieri: confronto con i dati dei Registri tumori.

OBJECTIVE: the study evaluates the accuracy of an algorithm based on hospital discharge data (HDD) in order to estimate breast cancer incidence in three italian regions (Emilia-Romagna, Toscana and Veneto) covered by cancer registries (CR). The evolution of computer-based information systems in health organization suggests automatic processing of HDD as a possible alternative to the time-consuming methods of CR. The study intends to verify whether HDD quickly provides reliable cancer incidence estimates for diagnosis and therapy evaluations. DESIGN AND SETTING: an algorithm based on discharge diagnosis and surgical therapy of hospitalized breast cancer patients was developed in order to provide breast cancer incidence. Results were compared with the corresponding incidence data of cancer registries. The accuracy of the automatic method was also verified by a direct record-linkage between HDD output and registries' files. The overall survival of cases lost to "HDD method" was analyzed. RESULTS: in the period covered by the study (3,125,425 person/year) CR enrolled 6,079 incident cases, compared to 6,000 cases recorded through the HDD flow. Incidence rates of the two methods (CR 194.5; HDD 192.0 x 100.000) showed no statistical differences. However, matched cases by the two methods were only 5,038. The sensitivity of the HDD algorithm was 82.9% and its predictive positive value (PPV) was 84.0%. False positive cases were 9.9%. On the other hand, 12.3% CR incident cases were not identified by the algorithm: these were mainly made up of older women, not eligible for surgical therapy. Their three-years survival was 62.0% vs 88.8% of the whole incidence group. CONCLUSION: HDD flow performance was similar to observations reported in the literature. The agreement between HDD and CR incidence rates is a result of a cross effect of both sensitivity and specificity limitations of the HDD algorithm. This can seriously impair the reliability of the latter method with regard to the evaluation of diagnostic and therapeutic strategies in cohort studies (i.e. the most effective approach to health setting in oncology).s.

A hierarchical modelling approach to assess multi pollutant effects in time-series studies.

When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the 'true' concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012.

[Gender difference and mortality in a cohort of heroin users in the Provinces of Modena and Ferrara, 1975-1999]. / Differenze di genere e mortalità in una coorte di eroinomani nelle province emiliane di Modena e Ferrara, 1975-1999.

OBJECTIVE: To analyse overall and cause-specific hazards of death in a cohort of heroin users, separately by gender, survival and other risk factors. DESIGN AND SETTING: Longitudinal study of intravenous heroin users; subjects were enrolled between 1975 and 1999 in public health services of the Provinces of Modena and Ferrara and were included in a treatment program. MAIN OUTCOME MEASURES: For each gender, age-standardized mortality rates and standardized mortality ratios (SMR) for all causes and for specific causes. Kaplan-Meier method was used for estimating survival probability and Cox regression model to estimate hazard ratios (HR) of death. RESULTS: in the cohort of 4.644 intravenous drug users, 801 deaths were observed. In both sexes, mortality due to AIDS was lower in subjects enrolled in 1990-99 than among those enrolled in 1980-89. Mortality caused by overdose was particularly high in males enrolled between 1995-99. (SMRs in males and females were respectively 12.12 (95% CI 11.22-13.08) and 20.26 (17.23-23.83). Survival probability at 20 years of observation was 62% (60% for males and 68% for females). Risk of death was highest in males, in subjects enrolled after age 25, in subjects with a low educational level and in unemployed persons. CONCLUSION: Gender and socioeconomic conditions are important determinants of mortality among heroin users. The increase in deaths from heroin overdose in subjects enrolled in the recent years requires particular attention.

[Potentially avoidable hospitalisation in Bologna, 1997-2000: temporal trend and differences by income level]. / Ospedalizzazione potenzialmente evitabile nella città di Bologna, 1997-2000: andamento temporale e differenze per livello di reddito.

OBJECTIVE: To describe the temporal trend of hospitalisations for Ambulatory Care Sensitive Conditions (ACSCs) from 1997 to 2000 in Bologna (Italy) and to analyze the association with the income level. DESIGN AND SETTING: We have selected two panels of ACSCs: eight conditions for the paediatric/young population (<20 years of age) and fourteen for the adult population (> or =20 years of age). All discharges for ACSCs of residents in Bologna from Emilia-Romagna hospitals have been selected in the years 1997-2000. An indicator of social position was computed: the median per capita equivalent income by census block, obtained through record linkage between the Italian Tax Register (income earned in 1998) and the Population Register of Bologna. MAIN OUTCOME MEASURES: The direct age-standardized rates and the rate ratio by income level have been calculated. The Poisson regression model has been used to calculate the relative risk (RR) of hospitalizations for ACSCs. RESULTS: 2359 (17.6% of the total) hospitalisations have been selected among the paediatric/young population and 27822 (11.1% of the total) among the adult population. The annual age-adjusted rate of ACSC is 122.68 per 10000 persons among children and 176.60 among adults. The hospitalisation forACSCs among children is associated with a middle-low level of income (RR 1.55; CI 95% 1.35-1.78 for the lowest level vs. highest level), male gender and age <5 years old. In the adult population the risk of hospitalisation for ACSCs is higher among those with lower levels of income (RR 1.80; CI 95% 1.66-1.95 for the lowest level vs. highest level), moreover the RR is higher for men at every age. The admissions forACSCs among adults show a decreasing temporal trend. CONCLUSIONS: The disadvantaged groups of the population experience the highest risk of hospitalisation for ACSCs, with differences by gender and age groups. Although it is difficult to specifically identify the mechanisms potentially involved in the relationship between socioeconomic status and excess of hospitalisation, the admissions for ACSCs may represent an interesting indicator of quality ofprimary care.

Modelling macronutrient dynamics in the Hampshire Avon river: A Bayesian approach to estimate seasonal variability and total flux.

The macronutrients nitrate and phosphate are aquatic pollutants that arise naturally, however, in excess concentrations they can be harmful to human health and ecosystems. These pollutants are driven by river currents and show dynamics that are affected by weather patterns and extreme rainfall events. As a result, the nutrient budget in the receiving estuaries and coasts can change suddenly and seasonally, causing ecological damage to resident wildlife and fish populations. In this paper, we propose a statistical change-point model with interactions between time and river flow, to capture the macronutrient dynamics and their responses to river flow threshold behaviour. It also accounts for the nonlinear effect of water quality properties via nonparametric penalised splines. This model enables us to estimate the daily levels of riverine macronutrient fluxes and their seasonal and annual totals. In particular, we present a study of macronutrient dynamics on the Hampshire Avon River, which flows to the southern coast of the UK through the Christchurch Harbour estuary. We model daily data for more than a year during 2013-14 in which period there were multiple severe meteorological conditions leading to localised flooding. Adopting a Bayesian inference framework, we have quantified riverine macronutrient fluxes based on input river flow values. Out of sample empirical validation methods justify our approach, which captures also the dependencies of macronutrient concentrations with water body characteristics.

Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles.

BACKGROUND: Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis. METHODS: We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002-2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005. RESULTS: The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of -3.5% (95% CI: -0.12%, -5.74%). CONCLUSIONS: We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.

Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas.

This paper describes a Bayesian hierarchical approach to predict short-term concentrations of particle pollution in an urban environment, with application to inhalable particulate matter (PM10) in Greater London. We developed and compared several spatiotemporal models that differently accounted for factors affecting the spatiotemporal properties of particle concentrations. We considered two main source contributions to ambient measurements: (i) the long-range transport of the secondary fraction of particles, which temporal variability was described by a latent variable derived from rural concentrations; and (ii) the local primary component of particles (traffic- and non-traffic-related) captured by the output of the dispersion model ADMS-Urban, which site-specific effect was described by a Bayesian kriging. We also assessed the effect of spatiotemporal covariates, including type of site, daily temperature to describe the seasonal changes in chemical processes affecting local PM10 concentrations that are not considered in local-scale dispersion models and day of the week to account for time-varying emission rates not available in emissions inventories. The evaluation of the predictive ability of the models, obtained via a cross-validation approach, revealed that concentration estimates in urban areas benefit from combining the city-scale particle component and the long-range transport component with covariates that account for the residual spatiotemporal variation in the pollution process.