Ben Armstrong PhD
- Ben Armstrong's Contacts
- Room S43
- 15-17 Tavistock Place
- WC1H 9SH
- T: +44 (0)207 9272232
- F: +44 (0)207 9272701
I am an applied medical statistician with long-standing interest in the application of statistics to environmental and occupational health. I joined the Environmental Epidemiology Unit in 1995, having previously been Associate Professor in the Department of Occupational Health at McGill University, Montreal. I formally retired in 2016, but continue a limited academic engagement on a part time basis.
I teach statistical methods for aplication to public health research in the LSHTM and various international courses. Because I am retired I no longer take new doctoral students as primary supervisor but am happy to contribute as member of supervisory teams.
My research interests cover most of environmental epidemiology and the statistical methods required for it. Specific methodological research includes that on the regression analysis of time series of health events and in particular interrupted time series, and on effects of measurement errors on estimates of exposure-health relationships. Current substantive research topics of interest, on which I work in collaboration with colleagues, focus mainly on the impacts of weather and climate change on health.
My Google Scholar published research profile is here. I was included in the Thompson-Reuters 2015 and 2016 lists of “highly cited researchers”, a classification based on number of publications from the pevious decade in the "top 1%" by citations.
- Climate change
- Occupational health
- Statistical methods
- Time Series
A penalized framework for distributed lag non-linear models.
Gasparrini, A. ; Scheipl, F. ; Armstrong, B. ; Kenward, M.G. ;
Methods to Estimate Acclimatization to the Urban Heat Island Effects on Heat- and Cold-Related Mortality.
Milojevic, A.; Armstrong, B.G.; Gasparrini, A.; Bohnenstengel, S.I.; Barratt, B.; Wilkinson, P.;
Environ Health Perspect, 2016; 124(7):1016-22
Investigating Uncertainty in the Minimum Mortality Temperature: Methods and Application to 52 Spanish Cities.
Tobías, A. ; Armstrong, B. ; Gasparrini, A. ;
Mortality risk attributable to high and low ambient temperature: a multicountry observational study.
Gasparrini, A.; Guo, Y.; Hashizume, M.; Lavigne, E.; Zanobetti, A.; Schwartz, J.; Tobias, A.; Tong, S.; Rocklöv, J.; Forsberg, B.; Leone, M.; De Sario, M.; Bell, M.L.; Guo, Y.L.; Wu, C.F.; Kan, H.; Yi, S.M.; de Sousa Zanotti Stagliorio Coelho, M.; Saldiva, P.H.; Honda, Y.; Kim, H.; Armstrong, B.;
Lancet, 2015; 386(9991):369-75
Time series regression model for infectious disease and weather.
Imai, C. ; Armstrong, B. ; Chalabi, Z. ; Mangtani, P. ; Hashizume, M. ;
Environ Res, 2015; 142:319-327
The effect of reduced street lighting on road casualties and crime in England and Wales: controlled interrupted time series analysis.
Steinbach, R.; Perkins, C.; Tompson, L.; Johnson, S.; Armstrong, B.; Green, J.; Grundy, C.; Wilkinson, P.; Edwards, P.;
J Epidemiol Community Health, 2015; 69(11):1118-24
Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.
Armstrong, B.G.; Gasparrini, A.; Tobias, A.;
BMC Med Res Methodol, 2014; 14(1):122
Models for the Relationship Between Ambient Temperature and Daily Mortality.
Epidemiology, 2006; 17(6):624-31
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