A5027193250- Climate Change and Health Impacts
- Air Quality and Health Impacts
- Global Health Care Issues
- Health disparities and outcomes
- Thermoregulation and physiological responses
- Health, Environment, Cognitive Aging
- Air Quality Monitoring and Forecasting
- Energy and Environment Impacts
- Smoking Behavior and Cessation
- COVID-19 epidemiological studies
- Birth, Development, and Health
- Advanced Causal Inference Techniques
- Health Systems, Economic Evaluations, Quality of Life
- COVID-19 impact on air quality
- Optimism, Hope, and Well-being
- Noise Effects and Management
- Insurance, Mortality, Demography, Risk Management
- Statistical Methods and Bayesian Inference
- Suicide and Self-Harm Studies
- Child Nutrition and Water Access
- Thermal Regulation in Medicine
- Data Analysis with R
- Healthcare Systems and Reforms
- Mosquito-borne diseases and control
- Kidney Stones and Urolithiasis Treatments
Royal Society of Tropical Medicine and Hygiene
2018-2025
London School of Hygiene & Tropical Medicine
2016-2025
University of London
2011-2024
University of Bristol
2024
Environmental Health
2008-2024
American Public Health Association
2024
Faculty of Public Health
2017-2024
Helmholtz Zentrum München
2023
Czech University of Life Sciences Prague
2023
Barcelona Institute for Global Health
2023
Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at clearly defined point in time. It increasingly being used to evaluate ranging from clinical therapy national public legislation. Whereas shares many properties regression-based approaches other epidemiological studies, there are range unique features data require additional methodological considerations. In this tutorial we...
BackgroundAlthough studies have provided estimates of premature deaths attributable to either heat or cold in selected countries, none has so far offered a systematic assessment across the whole temperature range populations exposed different climates. We aimed quantify total mortality burden non-optimum ambient temperature, and relative contributions from moderate extreme temperatures.MethodsWe collected data for 384 locations Australia, Brazil, Canada, China, Italy, Japan, South Korea,...
Environmental stressors often show effects that are delayed in time, requiring the use of statistical models flexible enough to describe additional time dimension exposure-response relationship. Here we develop family distributed lag non-linear (DLNM), a modelling framework can simultaneously represent dependencies and effects. This methodology is based on definition 'cross-basis', bi-dimensional space functions describes shape relationship along both predictor its occurrence. In this way...
The systematic evaluation of the results time-series studies air pollution is challenged by differences in model specification and publication bias.We evaluated associations inhalable particulate matter (PM) with an aerodynamic diameter 10 μm or less (PM10) fine PM 2.5 (PM2.5) daily all-cause, cardiovascular, respiratory mortality across multiple countries regions. Daily data on were collected from 652 cities 24 We used overdispersed generalized additive models random-effects meta-analysis...
Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially and delayed effects in time series data. This methodology rests on the definition of <em>crossbasis</em>, bi-dimensional functional space expressed by combination two sets basis functions, which specify relationships dimensions predictor lags, respectively. is implemented R package <b>dlnm</b>, provides functions perform broad range within DLNM family then help...
Time series regression studies have been widely used in environmental epidemiology, notably investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes mortality, myocardial infarction disease-specific hospital admissions. Typically, for both exposure outcome, data are available at regular time intervals (e.g. daily pollution levels mortality counts) aim is to explore them. In this article, we describe general features...
The two-stage time series design represents a powerful analytical tool in environmental epidemiology. Recently, models for both stages have been extended with the development of distributed lag non-linear (DLNMs), methodology investigating simultaneously and lagged relationships, multivariate meta-analysis, to pool estimates multi-parameter associations. However, application methods analyses is prevented by high-dimensional definition DLNMs. In this contribution we propose method synthesize...
Climate change can directly affect human health by varying exposure to non-optimal outdoor temperature. However, evidence on this direct impact at a global scale is limited, mainly due issues in modelling and projecting complex highly heterogeneous epidemiological relationships across different populations climates.We collected observed daily time series of mean temperature mortality counts for all causes or non-external only, periods ranging from Jan 1, 1984, Dec 31, 2015, various locations...
In this paper, we formalize the application of multivariate meta‐analysis and meta‐regression to synthesize estimates multi‐parameter associations obtained from different studies. This modelling approach extends standard two‐stage analysis used combine results across sub‐groups or populations. The most straightforward is for non‐linear relationships, described example by regression coefficients splines other functions, but methodology easily generalizes any setting where complex are multiple...
In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity modeling and interpreting such phenomena lies additional temporal dimension needed to express association, as risk depends on both timing past exposures. This type dependency defined here exposure–lag–response association. this contribution, I illustrate general statistical framework for associations, established through extension...
Measures of attributable risk are an integral part epidemiological analyses, particularly when aimed at the planning and evaluation public health interventions. However, current definition such measures does not consider any temporal relationships between exposure risk. In this contribution, we propose extended definitions within framework distributed lag non-linear models, approach recently proposed for modelling delayed associations in either linear or exposure-response associations. We...
BackgroundExposure to cold or hot temperatures is associated with premature deaths. We aimed evaluate the global, regional, and national mortality burden non-optimal ambient temperatures.MethodsIn this modelling study, we collected time-series data on from 750 locations in 43 countries five meta-predictors at a grid size of 0·5° × across globe. A three-stage analysis strategy was used. First, temperature–mortality association fitted for each location by use regression. Second, multivariate...
Background: Studies have examined the effects of temperature on mortality in a single city, country, or region. However, less evidence is available variation associations between and multiple countries, analyzed simultaneously. Methods: We obtained daily data 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, States, Canada). Two-stage analyses were used to assess nonlinear delayed relation mortality. In first...
Background: Few studies have examined variation in the associations between heat waves and mortality an international context. Objectives: We aimed to systematically examine impacts of on with lag effects internationally. Methods: collected daily data temperature from 400 communities 18 countries/regions defined 12 types by combining community-specific mean ≥90th, 92.5th, 95th, 97.5th percentiles duration ≥2, 3, 4 d. used time-series analyses estimate wave–mortality relation over lags 0–10...
Background: Heat waves have been linked with an increase in mortality, but the associated risk has only partly characterized. Methods: We examined this association by decomposing for temperature into a “main effect” due to independent effects of daily high temperatures, and “added” effect sustained duration heat during waves, using data from 108 communities United States 1987–2000. adopted different definitions heat-wave days on basis combinations thresholds duration. The main was estimated...
Interrupted time series analysis differs from most other intervention study designs in that it involves a before-after comparison within single population, rather than with control group. This has the advantage selection bias and confounding due to between-group differences are limited. However, basic interrupted design cannot exclude co-interventions or events occurring around of intervention. One approach minimizse potential such simultaneous is add so there both an intervention-control...
Background Heatwaves are a critical public health problem. There will be an increase in the frequency and severity of heatwaves under changing climate. However, evidence about impacts climate change on heatwave-related mortality at global scale is limited. Methods findings We collected historical daily time series mean temperature for all causes or nonexternal causes, periods ranging from January 1, 1984, to December 31, 2015, 412 communities within 20 countries/regions. estimated...
The time stratified case cross-over approach is a popular alternative to conventional series regression for analysing associations between of environmental exposures (air pollution, weather) and counts health outcomes. These are almost always analyzed using conditional logistic on data expanded case–control (case crossover) format, but this has some limitations. In particular adjusting overdispersion auto-correlation in the not possible. It been established that Poisson model with stratum...
The evidence and method are limited for the associations between mortality temperature variability (TV) within or days.We developed a novel to calculate TV investigated TV-mortality using large multicountry data set.We collected daily from 372 locations in 12 countries/regions (Australia, Brazil, Canada, China, Japan, Moldova, South Korea, Spain, Taiwan, Thailand, United Kingdom, States). We calculated standard deviation of minimum maximum temperatures during exposure days. Two-stage...
Objectives Several observational studies have suggested an association between high temperatures and all-cause mortality. However, estimates on more specific mortality outcomes are sparse, frequently assessed in using different analytical methods. Methods A time series analysis was performed 10 regions England Wales during the summers (June–September) of 1993–2006. Average percentage linear increases risk for a 1°C increase temperature above region-specific thresholds attributable deaths...