Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling

inter and intra tumoral heterogeneity in dna damage evaluated by comet assay in early breast cancer patients

Abstract There are no clinical tools to functionally assess degree of DNA damage in breast cancer. The comet assay is an accepted research tool for assessing DNA damage, however, most cancer studies have assessed lymphocytes as surrogate cells. The aim of this pilot study was to use the comet assay in early breast cancer directly in tumor tissue to compare DNA damage between and within traditionally defined subgroups, and to explore intra-tumoral heterogeneity. Scrapings of tumor and healthy breast tissue were obtained at primary surgery from 104 women. Comet assay was applied to quantitatively assess DNA damage, revealing substantial inter- and intra-subgroup variation. Marked intra-tumoral heterogeneity was evident across all subgroups. The degree of DNA damage for an individual could not be predicted by breast cancer subgroup. Comet assay warrants further study as a potential clinical tool for identification of tumoral DNA damage and ultimately, individualised use of DNA damaging therapy

Cohort profile: the Italian Network of Longitudinal Metropolitan Studies (IN-LiMeS), a multicentre cohort for socioeconomic inequalities in health monitoring.

PURPOSE: The Italian Network of Longitudinal Metropolitan Studies (IN-LiMeS) is a system of integrated data on health outcomes, demographic and socioeconomic information, and represents a powerful tool to study health inequalities. PARTICIPANTS: IN-LiMeS is a multicentre and multipurpose pool of metropolitan population cohorts enrolled in nine Italian cities: Turin, Venice, Reggio Emilia, Modena, Bologna, Florence, Leghorn, Prato and Rome. Data come from record linkage of municipal population registries, the 2001 population census, mortality registers and hospital discharge archives. Depending on the source of enrolment, cohorts can be closed or open. The census-based closed cohort design includes subjects resident in any of the nine cities at the 2001 census day; 4 466 655 individuals were enrolled in 2001 in the nine closed cohorts. The open cohort design includes subjects resident in 2001 or subsequently registered by birth or immigration until the latest available follow-up (currently 31 December 2013). The open cohort design is available for Turin, Venice, Reggio Emilia, Modena, Bologna, Prato and Rome. Detailed socioeconomic data are available for subjects enrolled in the census-based cohorts; information on demographic characteristics, education and citizenship is available from population registries. FINDINGS TO DATE: The first IN-LiMeS application was the study of differentials in mortality between immigrants and Italians. Either using a closed cohort design (nine cities) or an open one (Turin and Reggio Emilia), individuals from high migration pressure countries generally showed a lower mortality risk. However, a certain heterogeneity between the nine cities was noted, especially among men, and an excess mortality risk was reported for some macroareas of origin and specific causes of death. FUTURE PLANS: We are currently working on the linkage of the 2011 population census data, the expansion of geographical coverage and the implementation of the open design in all the participating cohorts

A Bayesian kriging model for estimating residential exposure to air pollution of children living in a high-risk area in Italy

A core challenge in epidemiological analysis of the impact of exposure to air pollution on health is assessment of the individual exposure for subjects at risk. Geographical information systems (GIS)-based pollution mapping, such as kriging, has become one of the main tools for evaluating individual exposure to ambient pollutants. We applied universal Bayesian kriging to estimate the residential exposure to gaseous air pollutants for children living in a high-risk area (Milazzo- Valle del Mela in Sicily, Italy). Ad hoc air quality monitoring campaigns were carried out: 12 weekly measurements for sulphur dioxide (SO2) and nitrogen dioxide (NO2) were obtained from 21 passive dosimeters located at each school yard of the study area from November 2007 to April 2008. Universal Bayesian kriging was performed to predict individual exposure levels at each residential address for all 6- to 12-years-old children attending primary school at various locations in the study area. Land use, altitude, distance to main roads and population density were included as covariates in the models. A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. We obtained a predicted mean level of 25.78 (±10.61) µg/m3 of NO2 and 4.10 (±2.71) µg/m3 of SO2 at 1,682 children’s residential addresses, with a normalised root mean squared error of 28% and 25%, respectively. We conclude that universal Bayesian kriging approach is a useful tool for the assessment of realistic exposure estimates with regard to ambient pollutants at home addresses. Its prediction uncertainty is highly informative and can be used for both designing subsequent campaigns and for improved modelling of epidemiological associations

Material deprivation as marker of health need

A relationship between socio-economic status and health has been widely documented both by individual-level and ecological regression studies. We addressed the problem known in the literature as using a material deprivation index as predictor of health needs and comparing results when adjusting or not the health outcome and the deprivation index for the same confounding variables. We focus on non-linear hierarchical models. We take as example the the issue of introducing socio-economic indicators in national or regional resources allocation formulas. We fitted a series of models with different data hierarchies to evaluate both the individual effect and the aggregate (census block) effect of material deprivation on heath status, disentagling the individual from the contextual effects. Individual mortality records came from the Florence census cohort 1991-1995 which is part of the Tuscan Longitudinal Study. Data on socio-economic factors derived from individual records of the 1991 census. Our results suggested that after adjusting for age, material deprivation is a good predictor of health needs both at individual and at aggregate level (census block). The presence of a contextual effect increases the interest in using deprivatin in the allocation formula, since it would permit a better distribution of resources to disadvantaged micro-areas. In the present paper, we stress the need to estimate the association between deprivation and health appropriately adjusting for age. The ideal goal would be having information at small geographical level on the joint distribution of age and deprivation to age-standardize both the response and the predictor. A temporary solution should be to regress crude mortality rates on deprivation and age. The current common practice, in absence of individual data, to regress standardized mortality on material deprivation may be inappropriate

Material deprivation as marker of health need

A relationship between socio-economic status and health has been widely documented both by individual-level and ecological regression studies. We addressed the problem known in the literature as using a material deprivation index as predictor of health needs and comparing results when adjusting or not the health outcome and the deprivation index for the same confounding variables. We focus on non-linear hierarchical models. We take as example the the issue of introducing socio-economic indicators in national or regional resources allocation formulas. We fitted a series of models with different data hierarchies to evaluate both the individual effect and the aggregate (census block) effect of material deprivation on heath status, disentagling the individual from the contextual effects. Individual mortality records came from the Florence census cohort 1991-1995 which is part of the Tuscan Longitudinal Study. Data on socio-economic factors derived from individual records of the 1991 census. Our results suggested that after adjusting for age, material deprivation is a good predictor of health needs both at individual and at aggregate level (census block). The presence of a contextual effect increases the interest in using deprivatin in the allocation formula, since it would permit a better distribution of resources to disadvantaged micro-areas. In the present paper, we stress the need to estimate the association between deprivation and health appropriately adjusting for age. The ideal goal would be having information at small geographical level on the joint distribution of age and deprivation to age-standardize both the response and the predictor. A temporary solution should be to regress crude mortality rates on deprivation and age. The current common practice, in absence of individual data, to regress standardized mortality on material deprivation may be inappropriate

L'uso dell'indice di deprivazione materiale in Epidemiologia descrittiva. Rapporti ISTISAN 07/50

Rapporti ISTISAN, 07/5