Our research covers a wide range of topics, including: methodologies, global health modelling, climate change and health, air pollution, and spatio-temporal modelling.

Focus areas: 

Statistical Methodologies

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 their statistical definition to penalised DLNM, implemented through generalised additive models (GAM). Other specific extensions implemented first the reduction of DLNMs to uni-dimensional summaries, useful to combine results from multi-location analyses, and then the computation of attributable risk measures. The framework has been formally assessed in a simulation study to model lagged associations in environmental time series data. 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.

The EHM-Lab has 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. Applications of these meta-analytical developments have been described in a tutorial on 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.

Research by the EHM-Lab has led to the development of 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. The statistical framework is based on conditional regression models, presented in a methodological article.

 

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. The EHM-Lab has 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 quantifying mortality impacts of heat and cold, 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), sulphur dioxide (SO2), and carbon monoxide (CO), in addition to the analysis of risks by pollution components. The MCC Network has also contributed research on COVID-19, specifically about the role of methodological factors on the SARS-CoV-2 transmission.

Climate and health

Temperature and mortality

Non-optimal outdoor temperature is one of the leading causes of health burden attributed to environmental factors, with both heat and cold associated with substantial impacts on mortality and morbidity. The EHM-Lab has led this research topic by developing state-of-the-art statistical framework and study designs, and then applying them in substantive studies.

A seminal article by the EHM-Lab first presented a multi-country analysis of excess mortality due to heat and cold, while other contributions assessed long-term effects of temperature and related risks for cause-specific mortality. The modelling framework has been extended in recent years, as illustrated in a more recent analysis of 854 urban areas across Europe, and in a small-area study of temperature-related risks. Parallel work has focused on the study of the optimal temperature across populations, first with a methodological contribution and then with a global analysis on the minimum mortality temperature (MMT). Another analysis evaluated the use of excess winter deaths as an impact measure for public health.

 

Analysis of vulnerability and adaptation factors

Health impacts of environmental and climate stressors vary dramatically both geographically and temporally, due to changes in vulnerability within and between populations. The issue is of primary interest for the definition of public health policies, in particular for identifying effective adaptation pathways to reduce the impact of climate change.

The EHM-Lab has provided several contributions to characterise differential vulnerability to heat and cold and to study potential adaptation strategies. We first assessed long-term variations in mortality  and then acclimatation processes leading to changes in risks within a season. We then investigated adaptive mechanisms related to heat and cold in a changing climate. Furthermore, we evaluated vulnerability factors responsible for differential risks of heat and cold, and the specifically assessed the role of air conditioning in decreasing heat impacts. We also linked large scale climatic teleconnections to annual variations in heat-related deaths. Finally, we explored ways to determine real-time health impacts for national heatwave plans.

 

Health impact projections of climate change

Climate change is the defining global issue of our time. A critical step in climate change research is to project impacts under different scenarios of greenhouse gas emissions, which should in turn inform alternative adaptation and mitigation policies. The EHM-Lab has contributed substantially to this topic.

We first developed a modelling framework for health impact projections, illustrated in a tutorial article that described the various steps and methodologies. We then applied such framework in applied studies to quantify the excess mortality due to heat and cold, both under various emission scenarios along the 21^st century and then for global mean temperature thresholds. A more recent study assessed the contribution of anthropogenic emissions to heat-related excess mortality in the historical period.

 

Assessment of weather-related risks

The assessment of climate-related health impacts requires the analysis of various weather indices. In addition to daily measures of dry-bulb temperature, the scientific literature includes studies on various indicators, which can inform both the definition of specific physiological pathways and the better characterisation of susceptibility profiles.

The EHM-Lab has led various epidemiological analyses on various weather indices, including assessment on the role of humidity in enhancing mortality risks, associations with alternative measures such as inter/intra-day variability, and composite indicators such as the universal thermal climate index (UTCI). Another important contribution was the assessment of climate reanalysis data for performing epidemiological studies on temperature-related risks.

Air pollution

Further details coming soon

Spatio-temporal modelling

Further details coming soon