Khaled Khatab

Drivers of U.S. toxicological footprints trajectory 1998–2013

By exploiting data from the Toxic Release Inventory of the United States, we have established that the toxicological footprint (TF) increased by 3.3% (88.4 Mt) between 1998 and 1999 and decreased by 39% (1088.5 Mt) between 1999 and 2013. From 1999 to 2006, the decreasing TF was driven by improvements in emissions intensity (i.e. gains in production efficiency) through toxic chemical management options: cleaner production; end of pipe treatment; transfer for further waste management; and production scale. In particular, the mining sector reduced its TF through outsourcing processes. Between 2006 and 2009, decreasing TF was due to decrease in consumption volume triggered by economic recession. Since 2009, the economic recovery increased TF, overwhelming the influence of improved emissions intensity through population growth, consumption and production structures. Accordingly, attaining a less-toxic economy and environment will be influenced by a combination of gains in production efficiency through improvement in emissions mitigation technologies and changes in consumption patterns. Overall, the current analysis highlights the structural dynamics of toxic chemical release and would inform future formulation of effective mitigation standards and management protocols towards the detoxification of the environment.

Social and Demographic Factors Associated with Morbidities in Young Children in Egypt: A Bayesian Geo-Additive Semi-Parametric Multinomial Model

Background Globally, the burden of mortality in children, especially in poor developing countries, is alarming and has precipitated concern and calls for concerted efforts in combating such health problems. Examples of diseases that contribute to this burden of mortality include diarrhoea, cough, fever, and the overlap between these illnesses, causing childhood morbidity and mortality. Methods To gain insight into these health issues, we employed the 2008 Demographic and Health Survey Data of Egypt, which recorded details from 10,872 children under five. This data focused on the demographic and socio-economic characteristics of household members. We applied a Bayesian multinomial model to assess the area-specific spatial effects and risk factors of co-morbidity of fever, diarrhoea and cough for children under the age of five. Results The results showed that children under 20 months of age were more likely to have the three diseases (OR: 6.8; 95% CI: 4.6–10.2) than children between 20 and 40 months (OR: 2.14; 95% CI: 1.38–3.3). In multivariate Bayesian geo-additive models, the children of mothers who were over 20 years of age were more likely to have only cough (OR: 1.2; 95% CI: 0.9–1.5) and only fever (OR: 1.2; 95% CI: 0.91–1.51) compared with their counterparts. Spatial results showed that the North-eastern region of Egypt has a higher incidence than most of other regions. Conclusions This study showed geographic patterns of Egyptian governorates in the combined prevalence of morbidity among Egyptian children. It is obvious that the Nile Delta, Upper Egypt, and south-eastern Egypt have high rates of diseases and are more affected. Therefore, more attention is needed in these areas.

Latent variable modelling of risk factors associated with childhood diseases: Case study for Nigeria

Abstract Objective To investigate the impact of various bio-demographic and socio-economic variables on joint childhood diseases in Nigeria with flexible geoadditive probit models. Methods Geoadditive latent variable model (LVM) was applied where the three observable disease (diarrhea, cough, fever) variables were modelled as indicators for the latent individual variable “health status” or “frailty” of a child. This modelling approach allowed us to investigate the common influence of risk factors on individual frailties of children, thereby automatically accounting for association between diseases as indicators for health status. The LVM extended to analyze the impact of risk factors and the spatial effects on the unobservable variable “health status” of a child less than 5 years of age using the 2003 Demographic and Health Surveys (DHS) data for Nigeria. Results The results suggest some strong underlying spatial patterns of the three ailments with a clear southeastern divide of childhood morbidities and this might be the results in the overlapping of the various risk factors. Conclusions Comorbidity with conditions such as cough, diarrhoea and fever is common in Nigeria. However, little is known about common risk factors and geographical overlaps in these illnesses. The search for overlapping common risk factors and their spatial effects may improve our understanding of the etiology of diseases for efficient and cost-effective control and planning of the three ailments.

Bayesian Geoadditive Mixed Latent Variable Models with Applications to Child Health Problems in Egypt and Nigeria

Childhood diseases and malnutrition are still a major cause of death of children in the developing world. This work focuses on investigating the impact of the important risk factors and geographical location on child morbidity and malnutrition in Egypt and Nigeria. Previous research has usually carried out separate regression analyses for certain diseases or types of malnutrition, neglecting possible association between them. Based on data from the Demographic and Health Survey of 2003, we apply recently developed geoadditive latent variable models, taking cough, fever and diarrhea as well as stunting and underweight as observable indicators for the latent variables morbidity and malnutrition. This allows studying the common impact of risk factors and geographical location on these latent variables, thereby taking account of association within a joint model. Our analysis identifies socio-economic and public health factors, nonlinear effects of age and other continuous covariates as well as spatial effects jointly influencing morbidity and malnutrition.

Risk factors associated with asbestos-related lung diseases among different sub-cohorts of formerly asbestos exposed workers in Germany

Objectives To examine the association between work-place exposure to asbestos and risk factors for developing asbestos related diseases using the analysis of a cross-sectional cohort of 8582 formerly asbestos exposed workers followed between March 2002 until the end of the year 2009. To assess the value of the three risk categories used for focused health surveillance programmes among formerly asbestos exposed power industry. Methods The work started with a descriptive analysis of three asbestos exposed sub-cohorts of workers from different types of the power industry then assessed the difference between the three sub-cohorts with regard to their corresponding level of risk. Further, we investigated how well the risk categories could predict the risk of asbestos related disease in comparison to applying a single risk factor such as age or asbestos exposure. Results The smokers over 65 age of years and those who had an exposure duration over 10 years were more likely to have lung cancer in this cohort. Hence the results showed that the age of participants (over 65 years, OR=11.47), smoking habits (OR=9.48 (current smoker), were associated significantly with lung cancer. The results showed that the risk of having lung cancer in group A was 1.87 times. Conclusions There was a strong association between both duration of exposure and age, and the risk of developing lung cancer. The use of risk categories based on a combination of risk factors may be an advantage for planning focused health surveillance programmes.

Serial measurements of mesothelin and osteopontin in formerly asbestos exposed power industry workers

Objectives For the proposed tumour markers mesothelin and osteopontin we determined the influence of gender, age and former asbestos exposure on blood concentrations, assessed the changes over time and investigated characteristics of marker concentrations in prediagnosis samples of patients with malignant pleural mesothelioma (MPM) and lung cancer (LC). Methods We analysed multiple blood samples of 2262 volunteers of three groups: 1894 formerly asbestos exposed power industry workers, 266 mostly asbestos exposed patients suffering from chronic respiratory diseases and 102 not occupationally exposed controls. Marker concentrations were determined using commercial ELISA kits. For statistical analysis we used the t-test and multiple regression models. Results We measured 3273 and 3218 concentrations of mesothelin and osteopontin respectively. Second, third and fourth samples were obtained in 691, 304 and 72 participants. 16 patients suffering from newly developed MPM or LC could be identified, seven of them had multiple prediagnosis samples. While marker concentrations were associated with age (mesothelin p=0.006, osteopontin p<0.001), gender and exposure duration had no influence. Asbestos exposed power industry workers with an overall increase of marker concentrations (mesothelin 53%, osteopontin 18%) showed mean annual increases of 0.176 nmol/l for mesothelin and 86 ng/ml for osteopontin. Conclusions The concentrations of mesothelin and osteopontin in asbestos exposed healthy individuals are strongly influenced by age. Fixed cut-off values for deciding between clinical work up and continued surveillance appear inadequate. Results would be consistent with an increase of the mesothelin concentration between 6 and 18 months before specific clinical symptoms develop.