Maëlle Salmon

Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: Design of the CHAI observational cohort study

While there is convincing evidence that fine particulate matter causes cardiovascular mortality and morbidity, little of the evidence is based on populations outside of high income countries, leaving large uncertainties at high exposures. India is an attractive setting for investigating the cardiovascular risk of particles across a wide concentration range, including concentrations for which there is the largest uncertainty in the exposure-response relationship. CHAI is a European Research Council funded project that investigates the relationship between particulate air pollution from outdoor and household sources with markers of atherosclerosis, an important cardiovascular pathology. The project aims to (1) characterize the exposure of a cohort of adults to particulate air pollution from household and outdoor sources (2) integrate information from GPS, wearable cameras, and continuous measurements of personal exposure to particles to understand where and through which activities people are most exposed and (3) quantify the association between particles and markers of atherosclerosis. CHAI has the potential to make important methodological contributions to modeling air pollution exposure integrating outdoor and household sources as well as in the application of wearable camera data in environmental exposure assessment.

A system for automated outbreak detection of communicable diseases in Germany

We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

Nosolink: An Agent-based Approach to Link Patient Flows and Staff Organization with the Circulation of Nosocomial Pathogens in an Intensive Care Unit

Abstract Computational models and simulations are commonly employed to aid decision making in two areas of health care management: optimization of the use of hospital resources and control of the spread of hospital-acquired infections caused by antibiotic-resistant pathogens. We propose a model that combines the operational and the epidemiologic perspectives to size up the effect of understaffing and overcrowding on nosocomial contagion in a intensive-care unit. Specifically, we develop an agent-based model simulating contact-mediated pathogen transmission which allows establishing quantitative relations between patient flow, nurse staffing conditions and pathogen colonization in patients. The results of the model, once calibrated with data from the literature, should indicate under which conditions the variation in pathogen transmission resulting from management decisions can lead to significant increases in the incidence of health care-associated infections in the intensive care unit.

Health impact assessment of cycling network expansions in European cities

We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits.

Predictors of Daily Mobility of Adults in Peri-Urban South India

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women’s activity spaces were smaller and more circular than men’s. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) μg/m3 for PM2.5 and 2.7 (0.5) μg/m3 for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies.

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM2.5 concentrations during June 2015–May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM2.5 concentration was ∼30 μg m−3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m−3) during 2–5% of observation days. Average concentrations were ∼25 μg m−3 higher during winter than during monsoon and ∼8 μg m−3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

Timeliness in the German surveillance system for infectious diseases: Amendment of the infection protection act in 2013 decreased local reporting time to 1 day

Time needed to report surveillance data within the public health service delays public health actions. The amendment to the infection protection act (IfSG) from 29 March 2013 requires local and state public health agencies to report surveillance data within one working day instead of one week. We analysed factors associated with reporting time and evaluated the IfSG amendment. Local reporting time is the time between date of notification and date of export to the state public health agency and state reporting time is time between date of arrival at the state public health agency and the date of export. We selected cases reported between 28 March 2012 and 28 March 2014. We calculated the median local and state reporting time, stratified by potentially influential factors, computed a negative binominal regression model and assessed quality and workload parameters. Before the IfSG amendment the median local reporting time was 4 days and 1 day afterwards. The state reporting time was 0 days before and after. Influential factors are the individual local public health agency, the notified disease, the notification software and the day of the week. Data quality and workload parameters did not change. The IfSG amendment has decreased local reporting time, no relevant loss of data quality or identifiable workload-increase could be detected. State reporting time is negligible. We recommend efforts to harmonise practices of local public health agencies including the exclusive use of software with fully compatible interfaces.