Our mission is to develop innovative epidemiological methods to study the impact of environmental stressors on human health.
We are a research team with complementary expertise in biostatistics, epidemiology, data science and climatology, based at the London School of Hygiene & Tropical Medicine.
The Environment and Health Modelling Lab is a team of researchers based in the Department of Public Health, Environments and Society at the London School of Hygiene & Tropical Medicine. We have multi-disciplinary expertise spanning biostatistics, environmental epidemiology, data science, statistical computing and climatology.
Our research aims to improve understanding of how environmental factors affect human health. Our work has a strong methodological focus and has contributed to the development of new study designs, statistical methods and modelling techniques for epidemiological analyses. We are exploring and pioneering the use of biostatistical tools and modern computing and data technologies to advance research in these fields.
Our research outputs cover a wide range of areas, including epidemiological studies on health risks associated with non-optimal temperature and air pollution, spatio-temporal modelling and environmental exposures, health impact projections under climate change scenarios and the use of new data technologies for environmental health studies.
Our research covers a wide range of topics, including: methodologies, global health modelling, climate change and health, air pollution, and spatio-temporal modelling.
Distributed lag linear and non-linear models
Distributed lag models (DLMs) represent an elegant methodology for describing lagged association in time series data. Originally developed in econometrics, they are now frequently used in epidemiological analysis. Pioneering work by Ben Armstrong extended them to distributed lag non-linear models (DLNMs) for non-linear temperature-mortality relationships.
We proposed a unified statistical framework for the DLM/DLNM class, based on the deﬁnition of a cross-basis, a bi-dimensional space of functions that describes the association simultaneously along the space of predictor and lag. Later, we generalised the methodology beyond time series data, allowing applications in various epidemiological fields. Finally, we extended the framework to penalised DLNM implemented through generalized additive models (GAM). The DLM/DLNM methodology is implemented in the R package dlnm.
An extended meta-analytical framework
Standard methods for meta-analysis are limited to pooling associations represented by a single effect size estimated from a set of independent studies. However, this setting can be too restrictive for modern meta-analytical applications.
We first contributed to developing multivariate meta-analytical methods for pooling multiparameter estimates representing complex associations. We then developed a general framework for meta-analysis based on linear mixed-effects models that includes, as special cases, multivariate, network, multilevel, dose-response, and longitudinal meta-analysis and meta-regression. This meta-analytical development has been applied in extended two-stage designs for environmental epidemiology. The methodology has been implemented in the R packages mvmeta and mixmeta.
- Study Designs
The case time series design
Modern linkage methods and data technologies provide a way to reconstruct detailed longitudinal profiles of health outcomes and predictors. This rich data setting, however, poses important methodological and computational problems that traditional epidemiological methods are not well suited to address.
We have developed the case time series (CTS) design, a novel methodology that combines the longitudinal structure typical of aggregated time series with the individual-level self-matched methods. The modelling framework is highly adaptable to various outcome and exposure definitions, and it is based on efficient methods that make it suitable for the analysis of highly informative longitudinal data resources.
The main article introduced the CTS design and illustrated applications in case studies using environmental and clinical data. A following tutorial article adapted the CTS methodology for the analysis of small-area data.
Small-area analysis of environmental risks
The increased availability of data on health outcomes and risk factors collected at fine geographical resolution makes possible conducting small-area epidemiological studies. However, this setting poses important methodological and computational issues, related to modelling complexities and data linkage.
The EHM-Lab has developed cutting-edge study designs for the analysis of small-area data. These methods allow the use of finely disaggregated health data linked with high-resolution environmental exposure measurements through GIS techniques. The framework offers the opportunity to study local variations in risk and the role of area-level characteristics in modifying the vulnerability to environmental stressors. We provided a methodological description of the design in a tutorial article, and an application to study small-area temperature-related risks.
Extensions of two-stage designs
The two-stage design has become a standard tool in environmental epidemiology to model multi-location data. We have recently proposed multiple design extensions of the classical two-stage design structure, all implemented within a unified analytical framework based on linear mixed-effects models.
The extended two-stage methodology, described in a recent tutorial article, permits the analysis of associations characterised by combinations of multivariate outcomes, hierarchical geographical structures, repeated measures, and/or longitudinal settings. We have applied it in various epidemiological analyses, including for modelling complex associations with temperature, investigating air pollution effects clustered at multiple geographical levels, assessing differential risks by age and geographical areas, quantifying excess mortality during the COVID-19 outbreak, and estimating the role of air conditioning in attenuating heat-related mortality.
Interrupted time series design
Interrupted time series (ITS) analysis is a valuable study design for evaluating the public health interventions. Its quasi-experimental nature allows quantifying effects of policies or events using a pre-post comparison while controlling for temporal trends.
We first illustrated the application of the ITS method for epidemiological analysis in a tutorial article that discussed design features and assumptions. Specific methodological contributions focused instead on model selection and the use of controls. The EHM-Lab has contributed to several applications of the ITS design, for instance for assessing the association between smoking bans and cardiovascular risk, the effect of the financial crisis on suicides, the impact of media coverage on the use of statins, the relationship between self-defence laws on firearm-related homicides, the effect of taxation on the sales of sugar-sweetened beverages, the impact of healthcare reforms on hospital care, and the excess mortality during the COVID-19 outbreak.
- The MCC Study
The EHM-Lab coordinates the Multi-Country Multi-City (MCC) Collaborative Research Network, an international collaboration of research teams aiming to produce epidemiological evidence on associations between environmental stressors, climate, and health. The research program benefits from the use of the largest dataset ever assembled for this purpose, including information on environmental exposures, health outcomes, and climate projections from hundreds of locations within several countries around the world.
Through MCC, we have led epidemiological analyses in several research areas. Initial studies focused on temperature-related risks, with the quantification of health impacts of heat and cold, the analysis of long-term and seasonal variation in risks, the role of humidity and inter/intra-day variability, long-term effects, and the minimum-risk temperature. Further studies first projected the mortality burden under future scenarios and for global temperature thresholds, and then quantified the impact of climate change in the historical period. More recent investigations assessed short-term risks of air pollutants in the largest multi-country analyses ever published, including studies on particulate matter (PM10 and PM2.5, as well as PM2.5-10), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO), in addition to the analysis of risks by pollution components.
- Climate and health
Further details coming soon
- Air pollution
Further details coming soon
- Spatio-temporal modelling
Further details coming soon
[Dataset] Temperature-related mortality exposure-response functions for 854 cities in Europe
This repository provides exposure-response functions by five age groups for most cities with more than 50,000 inhabitants in Europe. It includes coefficients and variance-covariance of B-spline bases to reconstruct the curves, simulations to represent uncertainty as well as city-specific temperature percentile.
Access the dataset on Zenodo
Read the publication
We provide the code for our analysis so our work can be applied elsewhere. Visit the links below:
New research from LSHTM's Environment and Health Modelling Lab and the Barcelona Institute for Global Health (ISGlobal) calls for greater utilisation of heat stress indices to better communicate the impact of dangerous heatwaves. Besides temperature, these indices take into account other meteorological factors such as humidity. The study assessed recent record-breaking heatwaves in Europe, North America and Asia, and found that the areas where the heat indices revealed the highest risk of heat stress did not necessarily coincide witht the regions with the highest recorded temperatures. Heat indices should be communicated to the public regularly, and authorities must act promptly to ensure the sufficient emergency response.
Read the publication
Dr Pierre Masselot, Research Fellow in Environmental Epidemiology and Statistics at LSHTM's Environment and Health Modelling Lab, comments on research assessing the impacts of heat and cold on mortality in 854 European cities, which showed that heat-related mortality risk among people aged 85 and over was higher in Paris than any other European city included in the study.
Read the article in Le Figaro, or the study published in The Lancet Planetary Health
A new published study, led by the EHM-Lab in collaboration with the MCC Collaborative Research Network and the EXHAUSTION project, has performed the most comprehensive analysis of heat and cold-related mortality in Europe. The researchers applied cutting-edge study designs and statistical methods and identified important geographical and age disparities in temperature impacts, indicating higher risks of heat and cold in older age groups and in Eastern Europe.
Full results and exposure-response functions derived for this study for 5 age groups in 854 European are publicly available in a Zenodo repository, with a semi-reproducible R code available on Github. See the Resources section for details.
Read the article
A new study by the MCC Collaborative Research Network, led by the EHM-Lab at LSHTM, investigated sulphur dioxide (SO2) mortality relationships in 399 cities across 23 countries in the period 1980-2019. The analysis revealed that short-term exposure to SO2 levels is linked with a measurable risk, and associated with substantial excess mortality even at levels below the current WHO daily limit (40 µg/m3).
Read the publication.
A new study using data from 93 European cities led by researchers from the Barcelona Institute for Global Health (ISGlobal) and LSHTM's Environment and Health Modelling Lab, showed that over four percent of deaths in cities during the summer months are due to urban heat islands, and one third of these deaths could be prevented by reaching a tree cover of 30%.
Researchers at the London School of Hygiene & Tropical Medicine are pioneering a new era of public health in the context of climate change and environmental degradation. In this video, Professor Antonio Gasparrini, Environment and Health Modelling Lab team lead, explains how mapping heat-related mortality can help improve understanding of the impacts of extreme heat and inform action to mitigate consequences.
New risk estimates suggest London and other urban areas had the highest heat-related mortality rate, while cold-related deaths were highest in Northern England, Wales and the South West.
Each year in England and Wales, there were on average nearly 800 excess deaths associated with heat and over 60,500 associated with cold between 2000 and 2019, according to a new study published in The Lancet Planetary Health.
Read more about the study.
Ammonium is one of the specific components of fine particulate matter (PM2.5), that has been linked to a higher risk of death compared to other chemicals found in it, according to a new study in the journal Epidemiology.
Find out more about the largest global analysis on air pollution.
Wildfire smoke is causing significant excess deaths globally, with the highest impacts in South-East Asia and Central America, according to the largest study of its kind in the Lancet Planetary Health.
Read more about the impact of wildfire smoke.
Between 1991 and 2018, more than a third of all deaths in which heat played a role were attributable to human-induced global warming, according to a new study in Nature Climate Change.
Read more on the study on global warming.
A novel method that combines artificial intelligence with remote sensing satellite technologies has produced the most detailed coverage of air pollution in Britain to date.
Read more about the ground-breaking technique.
Researchers from the Environment and Health Modelling Lab team teach LSHTM programme modules, as well as providing training and workshops internationally in a range of different research areas. Below is a list of LSHTM programmes and upcoming courses:
LSHTM MSc Courses
Swiss Epidemiology Winter School
Advanced Methods in Climate Change Epidemiology
Date: 16-18 January 2023
This course aims to provide a comprehensive overview of the latest developments in environmental epidemiology applied to climate change research. The course will cover state-of-the-art study designs such as multi-location time series analyses and small-area assessments, advanced methodologies such as distributed lag models and GIS data linkage, and applications such as health impact projection studies and health attribution analysis.
Course leads: Dr. Antonio Gasparrini, London School of Hygiene & Tropical Medicine, London, UK; Dr. Ana Maria Vicedo-Cabrera, University of Bern, Bern, Switzerland
European Educational Programme in Epidemiology: Residential Summer Course
Modern time series methods for public health and epidemiology (5 day course)
Date: 10-14 July 2023
This course will offer a thorough overview of established approaches and recent advancements in methods using time series data for health research, including a theoretical introduction as well as practical examples in public health, environmental, clinical, cancer, and pharmaco-epidemiology.
Course leads: Dr. Antonio Gasparrini, London School of Hygiene & Tropical Medicine, London, UK; Dr. Ana Maria Vicedo-Cabrera, University of Bern, Bern, Switzerland; and Dr. Francesco Sera, University of Florence, Florence, Italy
Please check back at a later date
The Multi-Country Multi-City (MCC) Collaborative Research Network: an international collaboration for global studies on environmental risks, climate change, and health, 34th Conference of the International Society for Environmental Epidemiology. 18–21 September 2022, Athens, Greece.